Time-varying Acceleration Coefficients Research Articles

Optimum dynamic dispatch of clean water, methane, electricity and heat considering carbon capture

This paper suggests a novel clean water, natural gas, heat and power generation system using wind turbine generators, PVT panels, bioenergy power plants, micro-hydro power plants, micro-cogeneration units (MCUs), battery energy storage system, thermal energy storage system and plug-in electric vehicles (PEVs). In this system, a power to gas technology consisting of a carbon capture unit, an electrolyzer, a hydrogen storage unit, and a methanation process, is used to supply the natural gas demand using CO2 captured from the MCUs and the hydrogen generated by the clean water electrolyzer. Furthermore, the electricity is consumed by a desalination unit to procure the clean water demand, carbon captured unit (CCU), electrolyzer water inflow, and fill up the water storage tank. At peak clean water demand periods, the excess water is pumped from the storage tank to the consumption side. Demand response program (DRP) is used for levelling the demand. Here, total cost for water-gas-heat-power nexus model throughout the day with and without DRP for three different scenarios is minimized by using quasi-oppositional fast convergence evolutionary programming, fast convergence evolutionary programming, self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients and differential evolution. It is seen from numerical results that cost attained with DRP is less than the cost attained without DRP.

An Improved DV-Hop Localization Algorithm Based on Human Conception Optimization with Time Varying Acceleration Coefficients for Wireless Sensor Network

An Improved DV-Hop Localization Algorithm Based on Human Conception Optimization with Time Varying Acceleration Coefficients for Wireless Sensor Network

A PSO-TVAC for optimal installation of multiple distributed generations in a radial distribution system

<span>The integration of distributed generations (DGs) into distribution system networks has seen a significant increase owing to the depletion of conventional energy resources and the growing power demand. However, a high penetration of DGs can adversely affect the stability of the distribution networks due to the intermittent nature of their generation capabilities. Hence, it is crucial to design DGs optimally to support grid voltage regulation and improve distribution networks performance. This study utilizes a particle swarm optimization (PSO) with time-varying acceleration coefficients (PSO-TVAC) technique to optimize the location and size of various distributed generation units while minimizing the total active power loss. The initial system power loss was determined using a distribution load flow analysis based on the backward-forward technique. The PSO-TVAC algorithm was then employed to identify the optimal placement and sizing of DGs within the standard IEEE 33-bus radial distribution network. To assess the proposed algorithm's effectiveness, photovoltaic (PV) and wind turbine (WT) were considered as the DGs. In comparison with other algorithms, PSO-TVAC achieved the lowest power loss, measuring 72.79 [kW] and 12.14 [kW] for <br /> 3-PV and 3-WT installations, respectively. Furthermore, the optimal installation of 3-PV and 3-WT improved the distribution system performance by 65.49% and 94.25%, respectively.</span>

Dynamic Optimal Power Flow for Grid-Connected Multi-Microgrid System considering Outage of Energy Sources

Recently multi-microgrid (MMG) system has attained additional attention because of significant assimilation of renewable energy sources, enhanced efficacy, reliability and stability through harmonization as well as cooperation of power swap amongst the microgrids (MGs). In this manuscript, dynamic optimal power flow (DOPF) for grid-connected MMG integrated demand response program (DRP) with and without energy source’s outage is presented. Total cost over the entire time-period considering instinctive characteristics of different phases is minimized. Power swap between grid and each MG is bidirectional. Each MG comprises small hydro power plants (SHPPs), solar PV plants (SPVPs), wind turbine generators (WTGs), bioenergy power plants (BPPs) and plug-in electric vehicles (PEVs). MMG used here comprises MG 1 including IEEE 33-bus system, MG 2 including 15-bus system and MG 3 including IEEE 69-bus system. This DOPF problem is solved by using quasi-oppositional fast convergence real coded genetic algorithm (QOFCRCGA), self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients (HPSO-TVAC), differential evolution (DE), artificial bee colony (ABC) optimization, fast convergence real coded genetic algorithm (FCRCGA) and real coded genetic algorithm (RCGA).

Applying Particle Swarm Optimization Variations to Solve the Transportation Problem Effectively

The Transportation Problem (TP) is a special type of linear programming problem, where the objective is to minimize the cost of distributing a product from a number of sources to a number of destinations. Many methods for solving the TP have been studied over time. However, exact methods do not always succeed in finding the optimal solution or a solution that effectively approximates the optimal one. This paper introduces two new variations of the well-established Particle Swarm Optimization (PSO) algorithm named the Trigonometric Acceleration Coefficients-PSO (TrigAc-PSO) and the Four Sectors Varying Acceleration Coefficients PSO (FSVAC-PSO) and applies them to solve the TP. The performances of the proposed variations are examined and validated by carrying out extensive experimental tests. In order to demonstrate the efficiency of the proposed PSO variations, thirty two problems with different sizes have been solved to evaluate and demonstrate their performance. Moreover, the proposed PSO variations were compared with exact methods such as Vogel’s Approximation Method (VAM), the Total Differences Method 1 (TDM1), the Total Opportunity Cost Matrix-Minimal Total (TOCM-MT), the Juman and Hoque Method (JHM) and the Bilqis Chastine Erma method (BCE). Last but not least, the proposed variations were also compared with other PSO variations that are well known for their completeness and efficiency, such as Decreasing Weight Particle Swarm Optimization (DWPSO) and Time Varying Acceleration Coefficients (TVAC). Experimental results show that the proposed variations achieve very satisfactory results in terms of their efficiency and effectiveness compared to existing either exact or heuristic methods.

Nonlinear Adaptive Back-Stepping Optimization Control of the Hydraulic Active Suspension Actuator

The displacement tracking performance of the electro-hydraulic servo actuator is critical for hydraulic active suspension control. To tackle the problem of slow time-varying parameters in the existing actuator dynamics model, a nonlinear adaptive back-stepping control (ABC) approach is adopted. Simultaneously, the parameters of the nonlinear ABC are difficult to configure, resulting in a poor control effect. An enhanced particle swarm optimization (PSO) approach integrating crazy particles (CP) and time-varying acceleration coefficients (TVAC) is suggested to optimize the controller settings. Furthermore, in order to obtain satisfactory dynamic characteristics of the transition process, the absolute value of the error time integral performance index is used as the minimum performance index function of parameter selection, and the square term of the control input is added to the performance index function to prevent excessive controller energy. Finally, it can be observed from the simulation results of the highest value emax of the displacement tracking error, the average value eμ of error, and the standard deviation eσ of error that the performance of the ABC parameters optimized by PSO+CP+ATVC is superior to the manually given ABC parameters. Therefore, this control method significantly improves the stability and speed of the control system. It provides a new research idea for the parameter optimization of controllers.

Modified Particle Swarm Optimization Based Powertrain Energy Management for Range Extended Electric Vehicle

The efficiency of hybrid electric powertrains is heavily dependent on energy and power management strategies, which are sensitive to the dynamics of the powertrain components that they use. In this study, a Modified Particle Swarm Optimization (Modified PSO) methodology, which incorporates novel concepts such as the Vector Particle concept and the Seeded Particle concept, has been developed to minimize the fuel consumption and NOx emissions for an extended-range electric vehicle (EREV). An optimization problem is formulated such that the battery state of charge (SOC) trajectory over the entire driving cycle, a vector of size 50, is to be optimized via a control lever consisting of 50 engine/generator speed points spread over the same 2 h cycle. Thus, the vector particle consisted of the battery SOC trajectory, having 50 elements, and 50 engine/generator speed points, resulting in a 100-D optimization problem. To improve the convergence of the vector particle PSO, the concept of seeding the vector particles was introduced. Additionally, further improvements were accomplished by adapting the Time-Varying Acceleration Coefficients (TVAC) PSO and Frankenstein’s PSO features to the vector particles. The MATLAB/SIMULINK platform was used to validate the developed commercial vehicle hybrid powertrain model against a similar ADVISOR powertrain model using a standard rule-based PMS algorithm. The validated model was then used for the simulation of the developed, modified PSO algorithms through a multi-objective optimization strategy using a weighted sum fitness function. Simulation results show that a fuel consumption reduction of 12% and a NOx emission reduction of 35% were achieved individually by deploying the developed algorithms. When the multi-objective optimization was applied, a simultaneous reduction of 9.4% fuel consumption and 7.9% NOx emission was achieved when compared to the baseline model with the rule-based PMS algorithm.

A Thermal Displacement Prediction System With an Automatic LRGTVAC-PSO Optimized Branch Structured Bidirectional GRU Neural Network

Considering technology’s rapid development, traditional manufacturing methods are insufficient to achieve the high accuracy demanded by aerospace, national defense, and numerous leading-edge engineering projects. Thermal displacement is a significant source of manufacturing errors, and accurately correcting such errors is difficult or even impossible using traditional machining methods. This paper proposes a machine learning method for high-accuracy error prediction that nonprofessionals can easily implement. An optimized automatic Logistic Random Generator Time Varying Acceleration Coefficient Particle Swarm Optimization (LRGTVAC-PSO) method is proposed to optimize a branch structured bidirectional Gated Recurrent Unit (GRU) neural network. The accuracy of the proposed method (with a three-axis average of 0.945) is superior to that of the other optimized algorithms evaluated in this study. The method serves as a means not only of accurately predicting thermal displacement but also of autotuning the hyperparameters of machine learning algorithms.

Scenario-based fuel-constrained heat and power scheduling of a remote microgrid

This manuscript suggests elephant clan optimization (ECO) algorithm to solve heat and electric power scheduling of a remote microgrid (MG) for three different scenarios considering fuel constraints. ECO algorithm is a populace-based method motivated by the elephants’ behaviour and their societal organization. MG comprises diesel generators (DGs), small hydro power plants (SHPPs), wind turbine generators (WTGs), solar micro-cogeneration (SMC) unit, biomass-fuel-fired micro-cogeneration (BMC) unit, battery energy storage system (BESS), plug-in electrical vehicles (PEVs) and thermal energy storage system (TESS). BMC units and SMC units are incorporated alternately into the MG. Numerical results of a typical system are compared with those obtained from self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients (HPSO-TVAC) and grey wolf optimization (GWO). It is seen from the evaluation that ECO offers superior solution.

Optimal day-ahead scheduling of renewable energy-based virtual power plant considering electrical, thermal and cooling energy

For synchronized functioning of distributed renewable energy resources, virtual power plant (VPP) is manifested as an upshot for maximizing net profit of the partakers taking into account uncertainties. Here, optimal day-ahead scheduling for VPP has been performed for meeting electrical, thermal and cooling demands. VPP comprises small hydro power plants, wind turbine generators, solar micro-cogeneration units, biomass-fuel-fired micro-cogeneration units, battery energy storage system, plug-in electric vehicles, electric chiller, absorption chiller, ice storage conditioner and thermal energy storage system. SMC units and BMC units are incorporated alternately. Demand response program is applied for electric, thermal and cooling demands to flatten demand curves and to improve system flexibility and decrease cost. The scheduling problem is solved by utilizing bottlenose dolphin optimizer (BDO), self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients (HPSO-TVAC) and grey wolf (GWO) optimization so as to maximize net profit of VPP operator. The maximum profit obtained from BDO is about 5.25 % and 9.82 % more than the maximum profit obtained from HPSO-TVAC and GWO respectively.

Simulation optimization for parcel hub scheduling problem in closed-loop sortation system with shortcuts

With the growth of e-commerce, the quantity of delivery orders and parcels is steadily increasing. Reliable and fast parcel delivery services are of utmost importance to customers. To achieve this target, the central parcel consolidation terminals (CPCT) known as parcel sorting hubs are dedicated to improving the operational efficiency especially in the optimal scheduling of inbound trucks. This study aims to determine the optimal schedule and assignment of inbound trucks to the inbound docks, known as the parcel hub scheduling problem with shortcuts (PHSPwS), for minimizing the expected makespan and expected waiting time of inbound trucks in the closed-loop automated sorting conveyor (ASC) system with shortcuts of the parcel sorting hubs. We first construct a mathematical formulation to present the PHSPwS problem based on the traffic control model. However, due to the congestion behavior on the conveyors and stochastic and dynamic nature of the ASC system, the expected objective functions are not analytically available from the mathematical model. Then, a simulation optimization (SO) approach combined with an adaptive genetic-based discrete particle swarm optimization (AGDPSO) algorithm is proposed to tackle the PHSPwS problem in a stochastic and dynamic ASC environment. The closed-loop ASC simulation model is first established to describe the detail operation of the closed-loop ASC sortation system with shortcut. Based on performance evaluation of this simulation model, a novelty algorithm AGDPSO, which hybridized genetic algorithm and particle swarm optimization algorithm with self-tuning parameters is developed to search and find the optimal inbound truck schedule in the large-scale solution space. In particular, the time-varying acceleration coefficients embedded in the AGDPSO are adopted to amplify the effectiveness of the global and local best solutions to obtain a better balance between exploration and exploitation of the searching space. The experimental results reveal that AGDPSO outperforms other algorithms and provides a better and more stable solution. Moreover, the impact of the strategic and operational factors of the closed-loop ASC system for the expected makespan and truck queueing time are also investigated, while the automatic truck unloading mode is confirmed to significantly improve the system efficiency.

Novel Parameter Identification Method for Basis Weight Control Loop of Papermaking Process

The basis weight control loop of the papermaking process is a non-linear system with time-delay and time-varying. It is impractical to identify a model that can restore the model of real papermaking process. Determining a more accurate identification model is very important for designing the controller of the control system and maintaining the stable operation of the papermaking process. In this study, a strange nonchaotic particle swarm optimization (SNPSO) algorithm is proposed to identify the models of real papermaking processes, and this identification ability is significantly enhanced compared with particle swarm optimization (PSO). First, random particles are initialized by strange nonchaotic sequences to obtain high-quality solutions. Furthermore, the weight of linear attenuation is replaced by strange nonchaotic sequence and the time-varying acceleration coefficients and a mutation rule with strange nonchaotic characteristics are utilized in SNPSO. The above strategies effectively improve the global and local search ability of particles and the ability to escape from local optimization. To illustrate the effectiveness of SNPSO, step response data are used to identify the models of real industrial processes. Compared with classical PSO, PSO with time-varying acceleration coefficients (PSO-TVAC) and modified particle swarm optimization (MPSO), the simulation results demonstrate that SNPSO has stronger identification ability, faster convergence speed, and better robustness.

Hybrid Global Optimization Algorithm for Feature Selection

This paper proposes Parallelized Linear Time-Variant Acceleration Coefficients and Inertial Weight of Particle Swarm Optimization algorithm (PLTVACIW-PSO). Its designed has introduced the benefits of Parallel computing into the combined power of TVAC (Time-Variant Acceleration Coefficients) and IW (Inertial Weight). Proposed algorithm has been tested against linear, non-linear, traditional, and multiswarm based optimization algorithms. An experimental study is performed in two stages to assess the proposed PLTVACIW-PSO. Phase I uses 12 recognized Standard Benchmarks methods to evaluate the comparative performance of the proposed PLTVACIW-PSO vs. IW based Particle Swarm Optimization (PSO) algorithms, TVAC based PSO algorithms, traditional PSO, Genetic algorithms (GA), Differential evolution (DE), and, finally, Flower Pollination (FP) algorithms. In phase II, the proposed PLTVACIW-PSO uses the same 12 known Benchmark functions to test its performance against the BAT (BA) and Multi-Swarm BAT algorithms. In phase III, the proposed PLTVACIW-PSO is employed to augment the feature selection problem for medical datasets. This experimental study shows that the planned PLTVACIW-PSO outpaces the performances of other comparable algorithms. Outcomes from the experiments shows that the PLTVACIW-PSO is capable of outlining a feature subset that is capable of enhancing the classification efficiency and gives the minimal subset of the core features.

Increasing Unbalanced Distribution Network’s Hosting Capacity for Distributed Energy Resources by Voltage Regulators

Renewable energy sources (RESs) are becoming promising nowadays. Deploying distributed energy resources (DERs) is thus increasing accordingly, but the intermittent nature associated with RES remains a significant challenge. Also, the distribution grid faces technological difficulties with the large-scale integration of PV generations and EV charging loads. This paper suggests a strategy for a novel optimal voltage regulator (VR) placement model associated with the optimal placement of electric vehicle chargers (EVCs) and photovoltaic (PV) cells. Also, VRs’ reference voltages are determined optimally to enhance the hosting capacity (HC) of three-phase unbalanced distribution feeders for large-scale residential/public EVCs and PVs. Two scenarios are defined to study the impact of VRs on the network’s HC for DERs, namely with and without VRs in the real three-phase unbalanced feeder. The developed model determines the optimal placement of VRs and their reference voltages linked with EVCs and PVs penetration. Time-Varying Acceleration Coefficients Iteration Particle Swarm Optimization (TVAC-IPSO) algorithm has been implemented to improve the real three-phase unbalanced distribution feeder’s HC for DERs. The efficiency of the proposed model and the solution method is validated on the modified IEEE 37-node medium voltage (MV) radial distribution network connected to 35 low voltage (LV) distribution feeders. The obtained results represent that installing a proper number of VRs will increase the network’s HC for DERs.

Frequency control scheme of an AC Islanded microgrid based on modified new self-organizing hierarchical PSO with jumping time-varying acceleration coefficients

Frequency control scheme of an AC Islanded microgrid based on modified new self-organizing hierarchical PSO with jumping time-varying acceleration coefficients

Techno-economic analysis of robust gas-to-power distributed generation planning for grid stability and environmental sustainability in Nigeria

With an average generating capacity of around 4000 MW of electricity for a population of over 200 million people, less than 50% of Nigerians have access to electricity. Nevertheless, Nigeria has one of the largest deposits of Natural gas, often disposed of by indiscriminate flaring. Natural gas has been classified as a clean-burning alternative energy source with enormous potential for meeting the immediate quest for sustainable energy transition worldwide. Thus, in this study, a techno-economic analysis to show the potential of investing in modern gas-to-power distributed generators (GtP-DGs) to enhance the electricity infrastructure and promote environmental sustainability in Nigeria was investigated. The grid stability limits and loss sensitivities are used for optimal locations for GtP-DGs and reactive power compensators along the existing grid. Particle swarm optimization with a time-varying acceleration coefficients algorithm is used to solve the formulated optimal gas and electricity power flow for optimal capacity sizing. Credible technical parameters of the grid’s operation in steady-state such as power loss, voltage magnitude, stability, and grid security, are analyzed using MATLAB®. Based on the obtained results, further investigation was done for economic and environmental impact assessments. The optimized amount of gas required for one hour of electricity was calculated to be about 9.060 × 106 CF with a financial saving of approximately USD 36,240 $. The percentage emission reduction expressed as a fraction of recovered gas fuel instead direct flaring is calculated as 14.12% for CO2, 0.34% for CH4 and 1.84% for N2O. The results show considerable technical and economic benefits and significant environmental sustainability paybacks for investment in robust natural gas technologies for immediate sustainable electrical energy production.

Dynamic optimal power flow for isolated microgrid incorporating renewable energy sources

Enhanced investiture to distribution generation (DG) had moved for alteration of existing networks for developing microgrid which can function autonomously or together with grid. Dynamic optimal power flow (DOPF) for isolated microgrid incorporating renewable energy sources has been studied here. The DOPF problem has been solved by using elephant clan optimization (ECO) algorithm, self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients (HPSO-TVAC) and grey wolf optimization (GWO). The IEEE 33-bus system, IEEE 69-bus system, IEEE 118-bus system and real existing isolated microgrid of 15-bus system have been used for validation. In all the four test systems, the cost obtained from ECO is the lowest among all the three techniques.

The Feasibility Assessment of Power System Dispatch with Carbon Tax Considerations

Traditional economic dispatch methods, which are used to minimize fuel costs, have become inadequate because they do not consider the environmental impact of emissions in the optimization process. By taking into account the horizon year load and carbon taxes, this paper examines the operation and dispatch of power units in a power system. The objective function, including the cost of fuels and the cost of carbon taxes, is solved by the modified particle swarm optimization with time-varying acceleration coefficient (MPSO-TVAC) method under operational constraints. Based on different load scenarios, the influences of various carbon taxes for the dispatch of units are simulated and analyzed. The efficiency and ability of the proposed MPSO-TVAC method are demonstrated using a real 345KV system. Simulation results indicate that the average annual CO2 emissions are 0.36 kg/kwh, 0.41 kg/kwh, and 0.44 kg/kwh in 2012, 2017 and 2022, respectively. As the capacity of gas-fired plants was increased in 2017 and 2022, the average cost in 2017 and 2022 doubled or tripled compared with the average cost in 2012. Reasonable solutions provide a practical and flexible framework for power sectors to perform feasibility assessments of power system dispatch. They can also be used to assist decision-makers in reaching minimal operation cost goals under the policies for desired emissions.

Hybrid neuro swarm intelligence paradigms for predicting the shear strength of sub-soil of heavy-haul freight corridor

This study aims to circumvent the operation of conducting laboratory tests of soil shear strength through a hybrid machine learning approach. The proposed approach integrates extreme learning machine (ELM) and particle swarm optimisation (PSO) with adaptive acceleration coefficients. Three hybrid ELMs, namely PSO optimised ELM with time-varying acceleration coefficients (ELM-TP), ELM optimised by improved PSO (ELM-IP), and ELM optimised by modified PSO (ELM-MP), have been established. Subsequently, the concept of mean PSO has been incorporated, and three additional hybrid models, namely ELM-TP integrated with mean PSO (ELM-TMP), ELM-IP integrated with mean PSO (ELM-IMP), and ELM-MP integrated with mean PSO (ELM-MMP), are constructed. The proposed concept is also used to construct six artificial neural network (ANN)-based hybrid models (i.e. ANN-TP, ANN-IP, ANN-MP, ANN-TMP, ANN-IMP, and ANN-MMP). Experimental results exhibit that the constructed ELM-IMP and ANN-IMP models can achieve the most desired accuracies in predicting the shear strength of soils.

Distribution network reconfiguration using time-varying acceleration coefficient assisted binary particle swarm optimization

The particle swarm optimization (PSO) algorithm is widely used to solve a variety of complicated engineering problems. However, PSO may suffer from an effective balance between local and global search ability in the solution search process. This study proposes a new acceleration coefficient for the PSO algorithm to overcome this issue. The proposed coefficient is implemented on the distribution network reconfiguration (DNR) problem to reduce power loss. The lowest power loss is obtained while problem constraints (maintain radial structure, voltage limits, and power flow balance) are satisfied with the proposed method. The validity of the proposed acceleration coefficient-based binary particle swarm optimization (BPSO) in handling the DNR problem is examined through simulation studies on IEEE 33-bus, P&G 69-bus, and 84-bus Taiwan Power Company (TPC) practical distribution networks. Furthermore, the DNR problem is evaluated regarding energy cost and environmental issues. Finally, the average computational times of the different acceleration coefficient-based PSO methods are compared. The solution speed of the proposed acceleration coefficient-based method is faster than the other methods in the DNR problem.