Multi-objective Particle Swarm Optimization Research Articles

An Approach to Near-Optimal Continuous-Thrust Solution for Plane Constellation Deployment

Paving the way in satellite constellation deployment, this article presents the Comprehensive Program (GCP) for planning constellation deployment missions by introducing a near-optimal solution for continuous-thrust maneuvers. The GCP establishes time constraints, enabling appropriate time scheduling and eliminating reconfiguration phases. It considers various cases of satellite departure and arrival sequences, collision avoidance constraints, and time limitations. A near-optimal solution for the continuous-thrust trajectory of each satellite is introduced, relying on the Fourier series approximation of the thrust pointing angle. The Fourier series with constant coefficients replaces the thrust angle in the satellite’s orbital motion equations in polar coordinates. The primary advantage of this near-optimal approach lies in its simplicity in accommodating space perturbations, posing no challenges in problem-solving. Additionally, the approach is beneficial due to its straightforward implementation and simple mathematical computations. This approach does not require pre-determining the revolution number of the transfer trajectory, unlike shape-based methods. The proposed method’s flexibility allows it to adapt and optimize the trajectory without prior assumptions about the number of revolutions required for the mission. Analyses demonstrate the proposed algorithm’s versatility and precision across various scenarios involving different orbital eccentricities, thrust forces, and thrust pointing angles. The constant coefficients of the Fourier series are determined by defining a set of objective functions and minimizing them using the Multi-Objective Particle Swarm Optimization algorithm (MOPSO). The first and second objective functions ensure that the transfer orbit reaches the desired deployment point, while the third and fourth ones guarantee the tangential conditions between the transfer orbit and the final orbit. An additional objective function minimizes the trajectory time. As a perturbation of interest, the effect of Earth’s oblateness is considered.

Influence and optimization of dual-orifice jet nozzles on oil capture performance of under-race lubrication with a radial oil scoop for high-speed bearings in aero-engine

The objective of this research is to determine the influence of dual-orifice jet nozzles on the oil–air flow dynamics and oil capture performance in the under-race lubrication for high-speed bearings and to identify the optimal design parameter combinations for optimizing the oil capture performance. An experimental setup for under-race lubrication is specifically designed, complemented by a multiphase computational fluid dynamics model to elucidate the oil–air flow behavior. The response surface methodology is combined with a multi-objective particle swarm optimization algorithm to perform multi-objective optimization of the oil capture performance. The findings indicate that oil capture efficiency initially rises to a peak and subsequently declines with increasing orifice spacing, identifying an optimal orifice spacing that maximizes efficiency. While the oil flow rate from dual-orifice jet nozzles is lower than twice that of single-orifice jet nozzles, appropriate parameter matching allowed for an increase in both captured lubricant oil and efficiency. Optimized configurations achieved substantial enhancements in oil capture efficiency, with the greatest improvement exceeding 16%, all while maintaining precise oil supply to the high-speed bearings.

Multi-objective optimization of high Mach waverider based on small-sample surrogate model

Advancements have been achieved in the optimization of waverider designs with the aid of machine learning to expedite the design process. However, these approaches are hampered by the need for extensive sample sizes and susceptibility to becoming ensnared in local optima. This study undertakes a parametric design based on the wedge-derived, power-law-shaped waverider, increasing configuration diversity and creating a dataset with limited samples by calculating waverider geometry and aerodynamic parameters. At a Mach number of 10, a multi-objective optimization design is implemented using the Young's double-slit experiment-least squares support vector regression (YDSE-LSSVR) surrogate model in conjunction with improved congestion distance multi-objective particle swarm optimization algorithm, focusing on maximizing the lift-to-drag ratio and volumetric efficiency as much as possible. The results indicated that, under conditions of limited samples, the YDSE-LSSVR model outperforms standard models such as support vector regression, LSSVR, Kriging, and Polynomial Chaos Expansions-Kriging regarding prediction accuracy. The Pareto solutions for both concave and convex waveriders, obtained through multi-objective optimization, improve the lift-to-drag ratio by 17.36% and 21.70%, respectively, and increase the volumetric efficiency by 88.89% and 105.56%, in comparison to baseline configurations. In addition, the research examines the impact of various design parameters on the Pareto solutions. Finally, the study applies the K-means method to conduct a cluster analysis of the Pareto solutions, generating three-dimensional waverider configurations based on distinguished solutions from different clusters.

Multi-objective Mathematical Model for Optimal Wind Turbine Placement in Wind Farm under Uncertainty

The main objective of this research is to introduce three energy risk management models grounded in optimization techniques for the strategic placement of wind turbines, considering wake effects and uncertainties in wind speed and direction. For this purpose, wind speed and direction data are gathered, and Monte Carlo simulation is employed to model the uncertainties. Subsequently, the risk management models undergo optimization using Non-Dominated Sorting Genetic Algorithm II (NSGA-II), Pareto envelope-based selection algorithm II (PESA-II), and Multi-Objective Particle Swarm Optimization (MOPSO) algorithms. Findings reveal that the wind farm's maximum power output reaches approximately 5.8 megawatts across all three algorithms and optimal turbine placements. A risk assessment was conducted using a tenth percentile criterion, revealing a significant production risk within the study area, with production falling below 1.8 megawatts in 90% of cases. Regarding the performance evaluation of the algorithms across all three models, superior performance in terms of solution proximity to the ideal solution is exhibited by PESA-II, while enhanced diversity and solution spread compared to the other algorithms are demonstrated by NSGA-II.

Comprehensive energy, exergy, and environmental evaluation of integrated heat pipe photovoltaic/thermal systems using multi-objective optimization

The depletion of fossil fuels and their environmental emissions have led to a tendency toward the implementation of sustainable energy sources. This study aims to evaluate the energy, exergy, and environmental performance of photovoltaic/thermal systems-based heat pipes. Furthermore, the electrical exergy and thermal efficiency of the photovoltaic/thermal system are analyzed. In this regard, two methods of multi-objective evolutionary algorithm based on non-dominated sorting genetic algorithm II (NSGA II) and multi-objective particle swarm optimization (MOPSO) are utilized. The simulated heat pipes are regarded as thermosyphon and pulsating type with water and acetone as the working fluid. The novelty of this study lies in the comparative analysis of thermosyphon and pulsating heat pipes with water and acetone as working fluids in PV/T systems, combined with the application of advanced multi-objective optimization algorithms, offering a comprehensive. The result demonstrates that acetone, as a working fluid, can help achieve better performance compared towater, and also pulsating heat pipe illustrates greater performance rather than the thermosyphon heat pipe. Moreover, the result reveals that the electrical exergy efficiency of the photovoltaic panel equipped with thermosyphon heat pipe with acetone has increased by 14.2 % compared to the photovoltaic panels without the heat pipe. In addition, utilizing thermosyphon base photovoltaic/thermal system with acetone reduces the production of greenhouse gases by 894.5 CO2eqkg/m2.year, which is a further reduction of 156.8 CO2eqkg/m2.year compared to the photovoltaic panel without a heat pipe.

Assessing the thermoeconomic performance of a solar-powered trigeneration system with an upgraded transcritical carbon dioxide unit

With the intervention of the industry in every aspect of human lifestyle on the one hand, and the prompt reduction in fossil fuel and their harms to the environment, including global warming, on the flip side, the importance of renewable sources of energy has been revealed more than ever. The presented study attempted to investigate a new configuration for a trigeneration configuration based on a solar renewable source. The proposed system comprises a parabolic trough solar collector segment to drive a transcritical carbon dioxide power and refrigeration subsystem, a dual-pressure organic Rankine cycle, and a thermal vapor compression process combined with a multi-effect desalination unit. The suggested configuration is carefully inspected from thermodynamic and economic perspectives, encompassing an analysis of a certain condition and a detailed parametric evaluation. Four decision parameters are employed for the parametric evaluation, in conjunction with two scenarios from a multi-objective particle swarm optimization combined with a linear programming method for multidimensional preference analysis as a decision-maker. The examination outputs bring out a net output power of 12.147 MW, a 7.707 MW cooling load, a 4.448 kg/s freshwater, and energetic and exergetic efficiencies of 15.286 % and 10.192 %, respectively. Moreover, examining the system's performance from an economic perspective reveals a total product cost rate of 954.249 $/h, leading to a 4.694-year payback period. This study optimizes energy usage and minimizes waste, offering industrial applications such as reducing fossil fuel dependence and lowering greenhouse gas emissions. It supports sustainable development and facilitates the global transition to cleaner energy sources.

Multi-objective coordinated optimization of low-carbon building energy systems based on high renewable energy penetration

In the context of China's targets to achieve a "carbon peak" by 2030 and "carbon neutrality" by 2060, the operation and maintenance of buildings with low-carbon and zero-energy consumption emerge as a critical technology for accomplishing the "dual carbon" goals in the building sector. As a novel energy supply system, low-carbon building energy systems can provide low-carbon, efficient, and economical energy supplies. Accordingly, this paper initially constructs a low-carbon building energy system based on a high penetration of renewable energy sources. Subsequently, considering the benefits of economic efficiency, carbon emissions, and renewable energy utilization, a multi-objective cooperative optimization model for low-carbon building energy systems with high renewable energy penetration is established. Finally, a multi-objective particle swarm optimization algorithm is applied to solve the model, and the system is studied and analyzed using a large domestic park as a case study. The results indicate that the low-carbon building energy system with high renewable energy integration, aiming for economic efficiency, carbon reduction, and increased renewable energy consumption, achieves supply energy costs of $ 19.2/m2 and carbon emissions of 49.1 kg/m2. These values represent reductions of $ 7.4/m2 and 60.9 kg/m2 compared to separate supply systems. Lastly, the study of the low-carbon building energy system provides theoretical references for renewable energy integration and energy transition in the building sector.

Dynamic switched crowding-based multi-objective particle swarm optimization algorithm for solving multi-objective AC-DC optimal power flow problem

In this paper, the multi-objective AC-DC optimal power flow (MO/AC-DC OPF) problem in the presence of renewable energy sources (RESs), flexible AC transmission system (FACTS) devices and multi-terminal direct current (MTDC) systems is introduced for the first time. Conflicting objective functions and the high complexity of the objective and constraint spaces are the main challenges in finding optimal solutions for MO/AC-DC OPF. To overcome these challenges, twelve different versions of the dynamic switched crowding-based multi-objective particle swarm optimization (DSC-MOPSO) algorithm are introduced in this paper. Studies on multimodal optimization problems have shown that all DSC-MOPSO versions have better performance metrics than the MOPSO algorithm. Using the developed DSC-MOPSO and its strong competitors, the Pareto-optimal solution sets of the MO/AC-DC OPF problem are investigated. In these investigations, the performances of the algorithms are tested for the minimization of dual and triple objectives such as fuel cost, voltage level deviation, emission and power loss in a modified IEEE 30-bus power grid. According to the simulation results, the proposed DSC-MOPSO achieved an improvement in fuel cost between 0.02 % and 5.05 % and a reduction in active power loss between 0.44 % and 30.74 % compared to its competitors. The Hypervolume (HV) performance metric was used to evaluate the Pareto-front coverage performance of DSC-MOPSO and other optimizers. The results from nine case studies of the MO/AC-DC OPF were statistically analyzed by the Friedman test according to the 1/HV metric. According to the Friedman test results, the rankings of DSC-MOPSO and MOMA are 1.984 and 3.079, respectively, ranking first and second among all competitors. Finally, in this study, feasible solutions for MO/AC-DC OPF problem are identified for the first time and the stability of competitive algorithms in finding these solutions is analyzed for the first time. The success rates and search times of DSC-MOPSO and MOMA algorithms in finding feasible solutions for MO/AC-DC OPF are 91.01 % (30.641 s) and 82.01 % (46.038 s), respectively.

Comparison of Optimization Methods for the Attitude Control of Satellites

The definition of multiple operational modes in a satellite is of vital importance for the adaptation of the satellite to the operational demands of the mission and environmental conditions. In this work, three optimization methods were implemented for the initial calibration of an attitude controller based on fuzzy logic with the purpose of performing an initial exploration of optimal regions of the design space: a multi-objective genetic algorithm (GAMULTIOBJ), a particle swarm optimization (PSO), and a multi-objective particle swarm optimization (MOPSO). The performance of the optimizers was compared in terms of energy cost, accuracy, computational cost, and convergence capabilities of each algorithm. The results show that the PSO algorithm demonstrated superior computational efficiency compared to the others. Concerning the exploration of optimum regions, all algorithms exhibited similar exploratory capabilities. PSO’s low computational cost allowed for thorough scanning of specific interest regions, making it ideal for detailed exploration, whereas MOPSO and GAMULTIOBJ provided more balanced performance with constrained Pareto front elements.

Data-driven multi-objective intelligent optimal control of municipal solid waste incineration process

The municipal solid waste incineration (MSWI) process has become the primary technology for municipal solid waste (MSW) treatment worldwide due to its advantages of harmlessness, reduction, and resource recovery. However, the automatic combustion control (ACC) system developed in developed countries are often not effectively applicable to other countries, particularly China, due to regional differences in MSW composition. To achieve intelligent development of MSWI plants, it is crucial to develop intelligent optimal control technology tailored to national conditions to reduce comprehensive pollutant emission concentration (CPEC) and improve combustion efficiency (CE). This article proposes a data-driven multi-objective intelligent optimal control strategy. Firstly, based on the analysis of the whole process, the Tikhonov regularization-least regression decision tree (TR-LRDT) algorithm is used to establish a MSWI whole process model, incorporating serial controlled objects and parallel pollutant indicators, to support a multi-objective optimization model. Then, leveraging the ACC system and expert experience, a multi-input multi-output loop controller is established using the single neuron adaptive PID (SNA-PID) algorithm to achieve stable control of key controlled variables. Next, a mutation crossover strategy and termination condition are integrated with the multi-objective particle swarm optimization (MOPSO) algorithm to solve the multi-objective optimization model, and domain expert rules are applied to determine the optimal setpoints for key controlled variables in the Pareto front, aiming to reduce CPEC and improve CE. Finally, the effectiveness of the proposed intelligent optimal control strategy is validated using actual data. The experimental results demonstrate that this strategy not only maintains the stability of key controlled variables but also decreases CPEC by 1.82% and increases CE by 2.38%, laying a foundation for further research into intelligent optimization control of the MSWI process. Further, the software system based on the proposed strategy is developed and validated on a hardware-in-loop simulation platform.

Piezoelectric Impedance-based Structural Damage Identification Empowered with Tunable Circuitry Integration and Multi-objective Optimization based Inverse Analysis

Abstract The piezoelectric impedance-based technique is increasingly recognized for its promise in structural health monitoring and damage identification. Built upon their self-sensing actuation capability, piezoelectric transducers can be integrated to host structures to acquire the system-level impedance information in high frequency range with small wavelength. Furthermore, the frequency-sweeping harmonic excitations in impedance measurements lead to the potential for model-based inverse identification of damage location and severity. A major challenge in damage identification, however, is that the inverse analysis is generally underdetermined, as the measurement information may not be adequate to yield a unique solution. In this research, a new methodology of tunable sensing in conjunction with multi-objective optimization inverse analysis is established. Taking advantage of the two-way electromechanical coupling of piezoelectric transducers, tunable inductance is integrated into the measurement circuit. For the same damage scenario, by tuning the inductance to a series of values, a family of impedance measurements can be acquired. Meanwhile, the inverse analysis is cast into a multi-objective optimization problem, aiming at minimizing the difference between measurement and model prediction and achieving sparsity in damage index vector. A Q-learning based multi-objective particle swarm optimization is synthesized to reach a small yet diverse solution set. We report the circuitry integration details as well as the algorithm enhancement with systematic case investigations. It is validated that the new methodology with enriched measurement can produce smaller solution set encompassing the true damage scenario, thereby providing vital information for diagnoses and prognosis.

Smart grid stability prediction model using two-way attention based hybrid deep learning and MPSO

In smart grid management, precise stability prediction is a complicated task that adds to the effective allocation of resources with grid stability. Specifically, demand-side management is considered an essential element of the overall Smart Grids system. Hence, predicting future energy demands is crucial to regulating consumption by aligning utility offerings with consumer demand. This research presents a hybrid deep learning model (Convolutional Neural Network [CNN] with Bi-LSTM) with a two-way attention method and a multi-objective particle swarm optimization method (MPSO) for short-term load prediction from a smart grid. The proposed hybrid model utilizes a two-way attention method at its encoding and decoding stages, in which an encoding attention layer helps to recognize all the essential features from an input vector, and a decoding attention layer helps to resolve the fixed context vector problem by offering better memory capacity. A CNN and Bi-LSTM are used to capture the essential features from the dataset. We also utilize a t-Nearest Neighbours algorithm to pre-process the initial dataset. An MPSO method combines the features of CNN and Bi-LSTM methods, resulting in better prediction accuracy. As far as we know, it is the first work to suggest a dynamic short-term load prediction model that considers different significant features and enables precise predicting outcomes. The performance of the proposed model and existing well-known deep learning models such as Recurrent Neural Network, Gated Recurrent Unit, Long Short-Term Memory (LSTM), Time Series Transformer, CNN-LSTM and various performance measuring parameters MAE, MSE, MAPE and RMSE are calculated on online UCI dataset (Electrical Grid Stability Simulated Dataset). The proposed hybrid model achieved a better prediction result, which proves the efficiency of the proposed model.

Optimization of Sailing Speed for Inland Electric Ships Based on an Improved Multi-Objective Particle Swarm Optimization (MOPSO) Algorithm

Sailing speed is a critical factor affecting the ship’s energy consumption and operating costs for a voyage. Inland waterways present a complex navigation environment due to their narrow channels, numerous curved segments, and significant variations in water depth and flow speed. This paper constructs a model of a ship’s energy consumption based on an analysis of ship resistance and the energy transfer relationship of ships. The K-means clustering algorithm is introduced to divide the Yangtze River waterway into multiple segments based on the similarity of navigation environments. Considering the constraints of the ship’s main engine and the desired arrival time, a multi-objective particle swarm optimization (MOPSO) algorithm, improved with cosine decreasing inertial weight and Gaussian random mutation, is employed to optimize segmented navigation speeds to achieve different goals. Finally, four cases are studied with a fully electric ship navigating the reaches of the Yangtze River. The results indicate that the optimized speed can reduce ship energy consumption by up to 6.18% and significantly reduce ship energy consumption and operational costs under different conditions.

Multi-objective preventive maintenance strategy and optimization considering unavailability and cost: A case study on VOBC

The Vehicle On-Board Controller (VOBC) is a core component of urban rail transit trains, with high maintenance costs and serious consequences in case of failure. Ensuring long-term VOBC operation while minimizing maintenance costs is of paramount importance. This paper proposes a multi-objective preventive maintenance (PM) strategy and optimization considering system unavailability and maintenance costs. Firstly, we provide a statement of the problem and analyze the impact of different maintenance actions on VOBC. Next, the decreasing process parameter is introduced to adjust the PM interval dynamically. The mathematical models of system unavailability and maintenance costs are derived to construct a multi-objective PM strategy. Then, we develop a hybrid heuristic algorithm including multi-objective particle swarm optimization and genetic algorithm (MOPSO-GA), which is used to obtain the optimal Pareto frontier for maintenance strategies. In the study, we select maintenance strategies by introducing an aversion factor to fulfill the preferences of different decision-makers. A comprehensive sensitivity analysis is performed to illustrate the impact of several critical parameters on the multi-objective PM strategy. The case study on Beijing subway’s VOBC is also executed. The outcomes indicate that the proposed method can dynamically adjust to match the preferences of diverse decision-makers and the MOPSO-GA algorithm has excellent robustness on most parameters. This approach allows for creating field maintenance strategies catering to the varied maintenance requirements of urban rail transit VOBC.

Design of Infinite Impulse Response Filters Based on Multi-Objective Particle Swarm Optimization

The goal of this study is to explore the effectiveness of applying multi-objective particle swarm optimization (MOPSO) algorithms in the design of infinite impulse response (IIR) filters. Given the widespread application of IIR filters in digital signal processing, the precision of their design plays a significant role in the system’s performance. Traditional design methods often encounter the problem of local optima, which limits further enhancement of the filter’s performance. This research proposes a method based on multi-objective particle swarm optimization algorithms, aiming not just to find the local optima but to identify the optimal global design parameters for the filters. The design methodology section will provide a detailed introduction to the application of multi-objective particle swarm optimization algorithms in the IIR filter design process, including particle initialization, velocity and position updates, and the definition of objective functions. Through multiple experiments using Butterworth and Chebyshev Type I filters as prototypes, as well as examining the differences in the performance among these filters in low-pass, high-pass, and band-pass configurations, this study compares their efficiencies. The minimum mean square error (MMSE) of this study reached 1.83, the mean error (ME) reached 2.34, and the standard deviation (SD) reached 0.03, which is better than the references. In summary, this research demonstrates that multi-objective particle swarm optimization algorithms are an effective and practical approach in the design of IIR filters.

Integration and Optimization of a Waste Heat Driven Organic Rankine Cycle for Power Generation in Wastewater Treatment Plants

The study focuses on achieving energy self-sufficiency in Wastewater Treatment Plants by proposing a comprehensive model for integrating, sizing, and optimizing an Organic Rankine Cycle system. The Organic Rankine Cycle system is designed to utilize waste heat from the gensets at As Samra Wastewater Treatment Plant in Jordan, where it will contribute to the overall electrical energy supply of the plant. Real data from As Samra Wastewater Treatment Plant is used to model and calculate the available waste heat using TRNSYS® software. The Organic Rankine Cycle model is then developed using ASPEN PLUS® software to explore the impact of operational parameters and determine their optimal values for maximizing the plant's energy profile. An economic analysis is conducted to assess the feasibility of the proposed model, considering system components, installation, operation, and maintenance costs. To optimize the Organic Rankine Cycle system, the study employs the Multi-Output Support Vector Regression technique to capture nonlinear relationships between independent variables (fluid type, turbine inlet pressure, turbine inlet temperature, turbine outlet pressure, and mass flow rate) and dependent variables (pump power input, waste heat input, and turbine specific work). The Osprey optimization algorithm is used to address the multi-objective optimization problem, with the proposed Pareto-based Osprey Optimization Algorithm and the Multi-Objective Particle Swarm Optimization technique being employed to evaluate critical performance and economic parameters such as system thermal efficiency, net power output, and the levelized cost of electricity. The results of the optimization strategies indicate that the M-SVR model's prediction accuracy is significantly improved after parameter optimization, with the model returning high R2 and low Mean Square Error values of 0.991 and 0.00216, respectively. The Pareto-Based Osprey Optimization Algorithm optimizer identifies the best working fluid as Isobutane/Isopentane in a ratio of 66:34, with optimal turbine inlet pressure and temperature of 15 bars and 218 °C, respectively. The Organic Rankine Cycle model at these optimal conditions achieves a cycle efficiency of 19.93 % and an Levelized Cost of Electricity value of 0.0353 USD/kWh.

A modified multi-objective particle swarm optimization (M-MOPSO) for optimal sizing of a solar–wind–battery hybrid renewable energy system

This study proposes and utilizes a modified multi-objective particle swarm optimization (M-MOPSO) algorithm for the optimal sizing of a solar-wind-battery hybrid renewable energy system for a rural community in Rivers State, Nigeria. Unlike previous studies that primarily focused on minimizing total economic cost (TEC) and total annual cost (TAC), this research emphasizes minimizing the loss of power supply probability (LPSP) and levelized cost of energy (LCOE). The M-MOPSO algorithm introduces a dynamic inertia weight, a unique repository update mechanism, and a dominance-based personal best update strategy, which collectively enhance its performance. Comparative analysis with PSO, NSGA-II, MOPSO and hybrid GA-PSO demonstrates that M-MOPSO consistently achieves a lower LPSP, although its LCOE remains higher. The M-MOPSO optimal configuration when simulated under various climatic scenarios was able to meet the energy needs of the community irrespective of ambient condition.

Multi-AUVs cooperative path planning in 3D underwater terrain and vortex environments based on improved multi-objective particle swarm optimization algorithm

This paper presents an improved multi-objective particle swarm optimization (IMOPSO) algorithm for cooperative path planning of multiple autonomous underwater vehicles (multi-AUVs) in 3D underwater terrain and vortex environment. Traditional PSO algorithms often struggle with local optima in high-dimensional, complex search spaces. To address this, we propose enhancements including Sine double-sequence perturbation interpolation for initial particle population generation, adaptive differential evolution strategies, iterative local search, and traction operations to enhance global search capability and avoid local optima. Additionally, cubic spline interpolation is employed to smooth the planned paths. The objective function is tailored to the ocean currents environment, considering path length, smoothness, deflection angle, and current impact, with a penalty function method to handle multi-AUVs cooperation and environmental constraints optimization for optimal energy consumption. Simulation results demonstrate that the proposed algorithm effectively plans short-range, high-security global paths for multi-AUVs, verifying its effectiveness and practicality in meeting the navigation requirements in ocean currents environments.

Eco-environmental dispatch of power system with high penetration wind farms considering demand/source side uncertainties

In this paper, a stochastic economic-emission dispatch (EE-D) of a power system with wind farms and flexible loads is proposed. The EE-D is defined as a stochastic multi-objective optimization problem, and a heuristic hybrid multi-objective algorithm is also proposed to solve it. The proposed algorithm is a combination of the lightning search algorithm and multi-objective particle swarm optimization (hLSA-MOPSO). The simulations are conducted in two sections. In the first section, the performance of the proposed algorithm is evaluated by optimizing standard benchmark functions. In the second section, the EE-D problem is solved optimally, considering the impact of uncertainties and the participation of flexible loads, as well as without considering them. Simulations are conducted on a standard 40-unit power system that includes two wind farms. Additionally, a sensitivity analysis is performed to examine the effects of uncertainties and flexible loads on cost and emissions. The sensitivity analysis demonstrates that increasing the penetration rate of wind farms results in reduced costs and emissions. However, it also reveals that the impact of this increase diminishes as the penetration rate continues to rise. Also, the uncertainty analysis reveals that the simultaneous uncertainty of load and wind has the most significant impact on costs and emissions.

6E analysis and particle swarm optimization of a novel ultra-high temperature solar cogeneration system fusing thermochemical energy storage and multistage direct heat transfer

The reshaping of the energy development model depends on the penetration of renewable energy on the input side and the substitution of electrical energy on the supply side. Therefore, this research proposes a novel solar cogeneration system in which the Brayton cycle possesses triple pressure and can be flexibly coupled directly to each subsystem under supercriticality without intermediate exchangers. Firstly, a series of research tasks are executed sequentially, including model establishment, cycle potential comparison, operating parameter analysis, and multi-objective particle swarm optimization. Secondly, the exergy flow rate, consumption rate, and 6E (energy, exergy, exergoeconomic, exergoenvironmental, emergoeconomic, and emergoenvironmental) performances of each subsystem and subcomponent are thoroughly discussed. Lastly, the operating performance, economic benefits, and regional characteristics of each subsystem and the cogeneration system are explored. After optimization, the exergy efficiency, average advanced exergy-emergy factor, and advanced exergy-emergy impact difference of the system are 22.72 %, 56.17 %, and 23.97 %. Compared with similar research, the energy efficiency of the system is improved by 8.2 %, which can repay the capital cost in 8.83 years, and the economic benefit reaches $5,503,086 for the entire service year. The system embodies excellent 6E performance, aligns with the goals of reshaping the energy development model, and provides guidelines for the development of ultra-high temperature solar cogeneration.