Pareto Optimality Theory Research Articles

Exploring the Scientific Mechanism of Tree Structure Network based on LiDAR Point Cloud Data

Abstract. To explore how trees optimize their structure, we developed a method based on Pareto optimality theory. This method consists of the following operations. Firstly, we utilize Quantitative Structure Models for Single Trees from Laser Scanner Data (TreeQSM) to extract tree structures from point clouds acquired through Light Detection and Ranging (LiDAR). Subsequently, we utilize a graph-theoretical model to characterize the natural tree structure networks and implement a greedy algorithm to generate Pareto optimal tree structure networks. Finally, based on the Pareto optimality theory, we explore whether tree structures are multi-objective optimized. This paper demonstrates that tree structures lie along the Pareto front between minimizing "transport distance" and minimizing "total length". The growth pattern of trees, which produces multi-objective optimized structures, is likely an intrinsic mechanism in the generation of tree structure networks. The location of tree structures along the Pareto front varies under different environmental conditions, reflecting their diverse survival strategies.

Pareto task inference analysis reveals cellular trade-offs in diffuse large B-Cell lymphoma transcriptomic data

One of the main challenges in cancer treatment is the selection of treatment resistant clones which leads to the emergence of resistance to previously efficacious therapies. Identifying vulnerabilities in the form of cellular trade-offs constraining the phenotypic possibility space could allow to avoid the emergence of resistance by simultaneously targeting cellular processes that are involved in different alternative phenotypic strategies linked by trade-offs. The Pareto optimality theory has been proposed as a framework allowing to identify such trade-offs in biological data from its prediction that it would lead to the presence of specific geometrical patterns (polytopes) in, e.g., gene expression space, with vertices representing specialized phenotypes. We tested this approach in diffuse large B-cell lymphoma (DLCBL) transcriptomic data. As predicted, there was highly statistically significant evidence for the data forming a tetrahedron in gene expression space, defining four specialized phenotypes (archetypes). These archetypes were significantly enriched in certain biological functions, and contained genes that formed a pattern of shared and unique elements among archetypes, as expected if trade-offs between essential functions underlie the observed structure. The results can be interpreted as reflecting trade-offs between aerobic energy production and protein synthesis, and between immunotolerant and immune escape strategies. Targeting genes on both sides of these trade-offs simultaneously represent potential promising avenues for therapeutic applications.

Multiobjective trajectory optimization algorithms for solving multi-UAV-assisted mobile edge computing problem

The Internet of Things (IoT) devices are not able to execute resource-intensive tasks due to their limited storage and computing power. Therefore, Mobile edge computing (MEC) technology has recently been utilized to provide computing and storage capabilities to those devices, enabling them to execute these tasks with less energy consumption and low latency. However, the edge servers in the MEC network are located at fixed positions, which makes them unable to be adjusted according to the requirements of end users. As a result, unmanned aerial vehicles (UAVs) have recently been used to carry the load of these edge servers, making them mobile and capable of meeting the desired requirements for IoT devices. However, the trajectories of the UAVs need to be accurately planned in order to minimize energy consumption for both the IoT devices during data transmission and the UAVs during hovering time and mobility between halting points (HPs). The trajectory planning problem is a complicated optimization problem because it involves several factors that need to be taken into consideration. This problem is considered a multiobjective optimization problem since it requires simultaneous optimization of both the energy consumption of UAVs and that of IoT devices. However, existing algorithms in the literature for this problem have been based on converting it into a single objective, which may give preference to some objectives over others. Therefore, in this study, several multiobjective trajectory planning algorithms (MTPAs) based on various metaheuristic algorithms with variable population size and the Pareto optimality theory are presented. These algorithms aim to optimize both objectives simultaneously. Additionally, a novel mechanism called the cyclic selection mechanism (CSM) is proposed to manage the population size accurately, optimizing the number of HPs and the maximum function evaluations. Furthermore, the HPs estimated by each MTPA are associated with multiple UAVs using the k-means clustering algorithm. Then, a low-complexity greedy mechanism is used to generate the order of HPs assigned to each UAV, determining its trajectory. Several experiments are conducted to assess the effectiveness of the MTPAs with variable population size and cyclic selection mechanisms. The experimental findings demonstrate that the MTPAs with the cyclic selection mechanism outperform all competing algorithms, achieving better outcomes.

Reliability-Constrained Task Scheduling for DAG Applications in Mobile Edge Computing

The development of the internet of things (IoT) and 6G has given rise to numerous computation-intensive and latency-sensitive applications, which can be represented as directed acyclic graphs (DAGs). However, achieving these applications poses a huge challenge for user equipment (UE) that are constrained in computational power and battery capacity. In this paper, considering different requirements in various task scenarios, we aim to optimize the execution latency and energy consumption of the entire mobile edge computing (MEC) system. The system consists of single UE and multiple heterogeneous MEC servers to improve the execution efficiency of a DAG application. In addition, the execution reliability of a DAG application is viewed as a constraint. Based on the strong search capability and Pareto optimality theory of the cuckoo search (CS) algorithm and our previously proposed improved multiobjective cuckoo search (IMOCS) algorithm, we improve the initialization process and the update strategy of the external archive, and propose a reliability-constrained multiobjective cuckoo search (RCMOCS) algorithm. According to the simulation results, our proposed RCMOCS algorithm is able to obtain better Pareto frontiers and achieve satisfactory performance while ensuring execution reliability.

A multi-objective fuzzy flexible job shop scheduling problem considering the maximization of processing quality

This paper analyzes practical production characteristics, including customer's stringent quality requirements and uncertain processing time in aircraft shaft parts manufacturing. Considering the above characteristics, we propose a multi-objective fuzzy aircraft shaft parts production scheduling problem considering the maximization of production quality. We define this problem as a multi-objective fuzzy flexible job shop scheduling problem (MO-fFJSP) with fuzzy processing time. To address this problem, we developed an improved multi-objective spider monkey optimization (IMOSMO) algorithm. IMOSMO integrates strategies such as genetic operators, variable neighborhood search and Pareto optimization theory on the framework of the conventional Spider Monkey Optimization (SMO) framework and discretize the continuous SMO algorithm to solve MO-fFJSP. To enhance the efficiency of the algorithm, we further adjust the sequence of the local leader learning phase and the global leader learning phase within the proposed IMOSMO framework. We conduct a comparative analysis between the performance of IMOSMO and NSGA-Ⅱ using 28 cases of varying scales. The computational results demonstrate the superiority of our algorithm over NSGA-Ⅱ in terms of both solution diversity and quality. Moreover, the performance of the proposed algorithm upgrades as the problem scale increases.

A response coordination design technology for the main loaded structures of the internal combustion engines based on NSEHO

AbstractResponse coordination design becomes an inevitable trend based on the multidisciplinary optimization design (MOD) in the development of the design for complex mechanical structures. Aiming at the problem of unclear mechanism of response coordination for main loaded structures, a response coordination design technology characterized by a response coordination evaluation system is proposed based on an analytic hierarchy process. In addition, a nondominated sorting elephant herding optimization (NSEHO) algorithm is proposed by introducing the Pareto optimization theory into elephant herding optimization and combining fixed‐sized candidate set adaptive random testing algorithm to overcome inherent limitations of the traditional algorithm in response coordination design. Taking the main bearing assembly structure as an example, the response coordination design is conducted based on the proposed technology and NSEHO algorithm. The response coordination design scheme of the main bearing assembly structure is experimentally verified. The results show that the Pareto frontier of NSEHO is smoother and is closer to the real Pareto frontier compared to NSGA‐II in condition of small population size and restricted iteration generations, revealing the advancement of the solving ability of NSEHO. The coordination coefficients of strength, stiffness, contact strength, and mass of the main bearing assembly structure are increased by 13.91%, 14.96%, 2.63%, and 0.07%, respectively, and the overall coordination coefficient are increased by 11.10%. The indicators of strength, stiffness, and contact strength of the response coordination scheme are better than those of the original scheme by experimental verification, demonstrating the effectiveness of the response coordination design technology. The proposed technology reveals the response coordination mechanism of the main loaded structures of internal combustion engines and provides an important guiding significance to MOD for complex mechanical structures.

Multi-objective task scheduling method for cyber–physical–social systems in fog computing

Cyber–physical–social systems refer to new systems that involve a large number of Internet of Things (IoT) devices connected through an interconnected network. These systems gather data for various services such as transportation, energy, healthcare, etc. This data is then transmitted to the cloud layer for storage, processing, and access. To handle the significant workload offloaded from resource-constrained IoT devices without latency or bandwidth issues, a technology called fog computing has emerged as an intermediary layer between the cloud and IoT devices. The quality of service (QoS) provided by fog computing relies on efficient task scheduling to optimize energy consumption and maximum execution time. This task scheduling problem is known to be NP-hard and cannot be solved with high precision in a reasonable time using traditional techniques or existing metaheuristic-based task schedulers. Therefore, this study proposes a new multi-objective task scheduling approach based on a modified marine predators algorithm (MMPA) to simultaneously minimize energy consumption and make-span under the Pareto optimality theory. This variant is named the multi-objective MMPA (M2MPA). M2MPA enhances the performance of the MPA by incorporating the polynomial crossover operator to improve its exploration operator and the adaptive CF parameter to enhance its exploitation operator. The effectiveness of M2MPA is evaluated using eighteen tasks with different scales (small, large, and medium) and heterogeneous workloads assigned to two hundred fog devices with varying processing speeds. Finally, M2MPA is compared with six well-established techniques using various performance indicators, including carbon dioxide emission rate, flowtime, make-span, and energy consumption. The experimental findings demonstrate the substantial superiority of M2MPA over all competing algorithms across different performance indicators and the Wilcoxon rank-sum test. Quantitatively, M2MPA achieves an average make-span of 10.095, average energy consumption of 3.77E+06, average flowtime of 8.39E+03, and average carbon dioxide emission rate of 8.88E+06.

Design and performance analysis of morphing nose cone driven by a novel bionic parallel mechanism for aerospace vehicle

To maintain the optimal aerodynamic configuration of morphing nose cone for aerospace vehicle according to environmental changes, honeybee abdomen, as a typical example that can adjusts configurations in different flight conditions adaptively, is a critical reference. Here we observe the muscle distribution of honeybee abdomen for revealing the deformation mechanism and a bionic parallel mechanism with distributed drive inspired by the muscle distribution between two adjacent segments is proposed. The kinematic analysis of the bionic parallel mechanism is performed to verify the deformation ability. To obtain the structural parameters, the multi-objective optimization is conducted using Pareto optimality theory. Then, kinematic performance and stiffness performance of the bionic parallel mechanism are analyzed in detail. On this basis, a distributed drive morphing nose cone for aerospace vehicle is designed. The morphing nose cone can change configurations in different environments like honeybee abdomen in accordance with kinematic analysis. Finally, the stiffness performance and aerodynamic performance are investigated to evaluate the morphing nose cone. This study provides new perspectives for the design of morphing aircraft.

High-speed railway track maintenance and irregularity rectification with coupling physical constraint of adjacent fasteners

Track smoothness maintenance of high-speed railway mainly depends on track fine tuning. Currently, track fine tuning relies mainly on the manual inspection conducted using track inspection trolley. Such an adjustment process is inefficient, time consuming, and contains discrepancies between the theoretical plan and actual effect. Therefore, the coupling physical constraint of adjacent fasteners (CPC-AF) are explained based on the finite element model, and an analytical calculation method based on the element stiffness matrix model is proposed. Meanwhile, while converting the deviation to irregularity, different starting positions of the reference chord affect the conversion result. Therefore, a track irregularity conversion error (TICE) correction constraint was proposed to overcome this effect. Based on the Pareto optimal theory, a ballastless track fine-tuning model was established. The experimental results show that the model can effectively optimize the track irregularity and its adjustment range under complex constraints. It addresses the issue of discrepancy between the theoretical fine-tuning plan and actual results.

Intelligent optimisation of an ultra-high-performance concrete (UHPC) multi-objective mixture ratio based on particle swarm optimisation

ABSTRACT To achieve specific construction requirements, such as the desired strength and flowability, the proportion of ultra-high performance concrete (UHPC) constituents must be carefully balanced. However, traditional concrete mixture design requires comprehensive trial-and-error experiments, which are cumbersome and costly. The requirements and objectives of a project may be contradictory, rendering the experimental design much more difficult. Therefore, to optimize the mixing ratio of UHPC, particle swarm optimization (PSO) was used to perform multi-objective optimization of the mechanical and flow properties of UHPC. The premise of comprehensive consideration of compressive strength and fluidity was given based on the Pareto optimality theory. The optimal ratio was validated through a combination of the macroscopic force phenomenon and microscopic mechanism. The findings show that the generated PSO algorithm model can simulate and analyze the complete relationship among all functional UHPC components, compressive strength, and fluidity and thus demonstrates its quite high accuracy and efficiency. The macro-micro test results indicate that the optimized UHPC ratio based on the PSO algorithm satisfies the best compressive strength and fluidity. The PSO algorithm can identify the direction for the optimization of the combination of UHPC multiple performance ratios.

Multi-objective optimization design of pile-anchor structures for slopes based on reliability theory considering the spatial variability of soil properties

Pile-anchor structures (PAS) are emerging as a promising tool for slope stabilization and landslide mitigation. However, the optimized design of PAS is an essential yet challenging issue. In particular, there is an irreconcilable contradiction between the safety and cost-efficiency in PAS design, and a coordinated balance between these two factors is critical for safe and practical applications. Previous optimization design studies rarely considered the uncertainty of PAS-reinforced slope systems while the spatial variability of soil properties has a significant impact on the optimization design. This study, therefore, proposes a framework for the multi-objective optimization design of PAS in slopes based on reliability theory and in consideration of the spatial variability of soil properties. The study is automated by coupling a multi-objective optimization design using Pareto optimality theory and PAS-reinforced slope simulations using Monte Carlo simulations to consider the spatial variability of soil properties. An illustrative example of the PAS design for a two-layer slope is provided to demonstrate the proposed framework. The results suggest that the optimal PAS design achieved meets the minimum failure probability and has the lowest total cost. This study aims to motivate the subsequent development of optimization design for slope engineering.

A Decentralized Cooperative Control Framework for Active Steering and Active Suspension: Multi-Agent Approach

A modular electric vehicle (EV) platform enables ability to integrate different control systems in a flexible way. In this article, a decentralized cooperative control framework is proposed to achieve the integration of active front steering (AFS) system and active-suspension system (ASS) by applying a multi-constrained distributed model predictive control (MDMPC) approach, which aims to improve the vehicle lateral stability, ride comfort, and roll safety during path tracking. First, a partly decoupled 6-degree-of-freedom (DoF) half-vehicle dynamics model is constructed. Then, a multi-agent-system (MAS)-based framework is introduced to coordinate AFS and ASS, which allows for the multi-constraints within the information exchange between agents. Through minimizing linear convex combination of objective functions, the cooperative control strategy is ultimately solved by the Pareto-optimality theory. Moreover, vehicle lateral and roll motion stability region described by the phase plane is employed to bound the controllable limits and achieve the dynamic cooperation between AFS and ASS. The simulation and hardware-in-the-loop (HIL) test results show that the proposed framework is effective for coordinating AFS and ASS, thereby enhancing the vehicle lateral and vertical stability during path tracking.

A Unified MIMO Optimization Framework Relying on the KKT Conditions

A popular technique of designing multiple-input multiple-output (MIMO) communication systems relies on optimizing the positive semidefinite covariance matrix at the source. In this paper, a unified MIMO optimization framework based on the Karush-Kuhn-Tucker (KKT) conditions is proposed. In this framework, with the aid of matrix optimization theory, <xref ref-type="theorem" rid="theorem1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Theorem 1</xref> presents a generic optimal transmit covariance matrix for MIMO systems with diverse objective functions subject to various power constraints and different levels of channel state information (CSI). Specifically, <xref ref-type="theorem" rid="theorem1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Theorem 1</xref> fundamentally reveals that for a diverse family of MIMO systems, the optimal transmit covariance matrices associated with different objective functions under various power constraints can be derived in a unified generic water-filling-like form. When applying <xref ref-type="theorem" rid="theorem1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Theorem 1</xref> to the case of multiple general power constraints, we firstly equivalently transform multiple power constraints into a single counterpart by introducing multiple weighting factors based on Pareto optimization theory. The optimal weighting factors can be found by the proposed modified subgradient method. On the other hand, for the imperfect MIMO system with statistical CSI errors, we firstly address the non-convexity of the robust optimization problem by following the idea of alternating optimization. Finally, our numerical results verify the optimal solution structure in <xref ref-type="theorem" rid="theorem1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Theorem 1</xref> and the global optimality of the proposed modified subgradient method, as well as demonstrate the performance advantages of the proposed alternating optimization algorithm.

Multi-Objective and Parallel Particle Swarm Optimization Algorithm for Container-Based Microservice Scheduling.

An application based on a microservice architecture with a set of independent, fine-grained modular services is desirable, due to its low management cost, simple deployment, and high portability. This type of container technology has been widely used in cloud computing. Several methods have been applied to container-based microservice scheduling, but they come with significant disadvantages, such as high network transmission overhead, ineffective load balancing, and low service reliability. In order to overcome these disadvantages, in this study, we present a multi-objective optimization problem for container-based microservice scheduling. Our approach is based on the particle swarm optimization algorithm, combined parallel computing, and Pareto-optimal theory. The particle swarm optimization algorithm has fast convergence speed, fewer parameters, and many other advantages. First, we detail the various resources of the physical nodes, cluster, local load balancing, failure rate, and other aspects. Then, we discuss our improvement with respect to the relevant parameters. Second, we create a multi-objective optimization model and use a multi-objective optimization parallel particle swarm optimization algorithm for container-based microservice scheduling (MOPPSO-CMS). This algorithm is based on user needs and can effectively balance the performance of the cluster. After comparative experiments, we found that the algorithm can achieve good results, in terms of load balancing, network transmission overhead, and optimization speed.

A Distributed Integrated Control Architecture of AFS and DYC Based on MAS for Distributed Drive Electric Vehicles

Reconstitution of control architecture creates a great challenge for distributed drive electric vehicles (DDEV), due to the emergence of a new distributed driving strategy. To this end, a novel distributed control architecture is proposed in this paper for integrated control of active front steering (AFS) system and direct yaw moment (DYC) system. First, a multi-agent system (MAS) is employed to construct a general framework, where AFS and DYC act as agents that work together to improve vehicle lateral stability and simultaneously reduce workloads of drivers during path tracking. The cooperative control strategies of two agents are obtained through Pareto-optimality theory to ensure optimal control performance of AFS and DYC. Then, on the basis of dynamic interaction between agents, terminal constraints, including terminal cost function and terminal input with local static feedback, are designed to guarantee the asymptotic stability of the close-loop system. Finally, virtual simulations are conducted to evaluate the proposed controller. The results indicate that the proposed control architecture can effectively preserve vehicle stability and reduce workloads of drivers, especially for the inexperienced driver. Furthermore, the hardware-in-loop (HIL) test results also demonstrate the feasibility of the proposed controller.

Multi-objective stochastic operation of multi-microgrids constrained to system reliability and clean energy based on energy management system

The paper presents the multi-objective operation of multi-microgrids (MGs) based on an energy management system (EMS) to achieve MGs with high reliability, clean energy, and optimal operation mode. The proposed problem minimizes the expected operating cost of the MG and non-renewable energy sources (NRESs), the expected energy not-supplied (EENS) as the reliability index, expected environmental emission level, and voltage deviations function (VDF) in different objective functions. Furthermore, the scheme is constrained to AC optimal power flow (AC-OPF) equations in MGs, reliability constraints, and mathematical models of power sources and active loads (ALs). Also, stochastic programming based on the combined Monte Carlo Simulation (MCS) method and Kantorovich technique is utilized to model uncertainties of the load, energy price, RESs generation power, energy demand of some ALs, and availability of MGs equipment. Moreover, the problem is converted into a single-objective problem using the Pareto optimization theory based on the ε-constraint method. The hybrid grey wolf optimization (GWO) and Teaching-Learning-based Optimization (TLBO) is then used to reach a reliable optimal solution. Finally, the suggested strategy is simulated on a standard system, where the numerical results verify capabilities of the scheme in improving operation and reliability of multi-MGs along with reducing environmental emission level.

Unified IRS-Aided MIMO Transceiver Designs via Majorization Theory

In this paper, we develop a unified framework for IRS-aided transceiver designs under general power constraints in multiple-input multiple-output (MIMO) systems which implement interference (pre-)subtraction via Tomlinson-Harashima precoding (THP) or Decision Feedback Equalization (DFE) technologies. Armed with majorization theory, two fundamental classes of performance criteria, namely $K$ -increasing Schur-concave and Schur-convex functions of the logarithm of Mean Square Error (MSE) of the data stream, are investigated in depth. Firstly, we propose a simplified counterpart of the optimal transceiver design under general power constraints, with equivalence guaranteed by Pareto optimization theory and Lagrange duality. Moreover, the optimal semi-closed form solution to this simplified transceiver design can be attained using the modified subgradient method. Next, we prove that for any Schur-concave objective, the optimal nonlinear THP (DFE) design is in essence the linear precoding (equalization). For any Schur-convex objective, the optimal transceiver design results in individual data streams with equal MSEs, and thereby reduces to the Gaussian mutual information maximization based design. Based on the above conclusions, we further propose an efficient alternating optimization algorithm to decouple the optimization of the transmit precoder and the IRS reflection coefficients, where the classical successive convex approximation (SCA) technique is applied to fight against non-convex subproblems. From the low computational complexity perspective, a two-stage scheme is also developed inspired by the capability of the IRS in constructing favorable wireless links. Finally, numerical results show the global optimality of the modified subgradient method and the excellent performance of the proposed alternating optimization algorithm and two-stage scheme.

Pareto optimality and game theory for pile design having conflicting objectives

ased on concept of Pareto-optimal solution and game theory associated with Nash non-cooperative and cooperative solution, a mathematical procedure is presented for optimum design of axially loaded pile structure. The decision making situation is formulated as a constrained optimization problem with two objectives of contradictory in nature. The factor of safety is taken as the design variable. Geometric constraints are considered by imposing a lower and upper bound on the design variable. Two objectives considered are: maximization of ultimate load carrying capacity of pile and minimization of associated cost. The generation of Pareto-optimal solution and methodology based on game theory concept is described. The design problem is mathematically formulated as two-person game. To obtain the starting point of game, Nash non-cooperative solution or Nash equilibrium solution is evaluated for an irrational play. For cooperative game, a negotiation model is developed for overall benefit of all players. Game is terminated when the optimal trade-off between two objectives is reached with maximization of supercriterion. Two numerical examples of practical interest are solved to demonstrate the methodology.

Pareto optimality and game theory approach for optimal deployment of DG in radial distribution system to improve techno-economic benefits

This paper discusses the application of nature-inspired swarm intelligence methods for optimal allocation and sizing of distributed generation (DG) in the radial distribution system (RDS). Introducing DG units in the RDS will enhance the technical and economic benefits if they are optimally deployed. The objective functions considered are to improve the technical aspects and net economical saving cost with DG units integration on RDS. In this paper, a weighted multi-objective index considers a wide range of technical issues such as active and reactive power losses of the system, voltage profile, line loading, and the voltage stability, these are assumed as technical improvement aspects in the RDS. A recent optimization method, i.e. improved raven roosting optimization (IRRO) algorithm has been implemented for optimal deployment of DG in RDS. The state of the art of IRRO algorithm parameters will improve the ability for exploration and prevent premature convergence. Pareto optimality is used in making a set of the best solutions between two conflicting objectives considered, i.e. technical and economical aspects. The main contribution in this paper is to utilize a game theory based (minimax) algorithm in taking the best decision from a set of non-dominated solutions obtained by Pareto optimality criteria. IEEE 33-bus and 69-bus RDS’s are considered as the test systems for verifying the effectiveness of the IRRO algorithm. A comparative analysis with other nature-inspired swarm optimization techniques such as particle swarm optimization (PSO), modified teaching learning based optimization (MTLBO), Jaya algorithm (JAYA), and grey wolf optimizer (GWO) is also presented in this work. The simulation results of IRRO are compared with similar existing papers. It is observed that the IRRO algorithm can produce better results for the considered multi-objective functions. The MATLAB software is employed for the purpose. The novelty of the paper lies in the use of Pareto optimal and game theory in obtaining better results to the problem of optimal deployment of DG in RDS to improve technical as well as economic benefits.

Optimized sizing of a standalone PV-wind-hydropower station with pumped-storage installation hybrid energy system

The development and utilization of renewable energy sources can not only effectively reduce carbon dioxide emissions, but also provide access to electricity to more parts of the world. A standalone hybrid system based on renewable sources is a promising way to supply reliable and continuous power in remote areas to which the grid has not extended. This paper designs and investigates a photovoltaics (PV)-wind-hydropower station with pumped-storage installation (HSPSI) hybrid energy system in Xiaojin, Sichuan, China as case of study. HSPSI can use the available flow of the river and store surplus energy generated from wind and PV by pumping water from the lower reservoir to the upper one. From the perspective of the investors, the techno-economic index is usually used to design the PV-wind-HSPSI hybrid energy system which aims to find the optimal configure with maximum power supply reliability and minimum investment cost. The trade-off analysis between the two objectives is based on Pareto optimality theory by means of Multi-Objective Particle Swarm Optimization (MOPSO). Besides, this paper takes the curtailment rate (CR) of the wind and PV power into consideration due to policy requirements. The relationship between the two objectives under various CR are analyzed and compared. Several results can be obtained as follows: 1) Comparing with the PV-HSPSI and wind-HSPSI hybrid energy system, the levelized cost of energy (LCOE) of the PV-wind-HSPSI hybrid energy system can reduce by 32.8% and 45.0% respectively. 2) For the PV-wind-HSPSI hybrid energy system, the LCOE can be as low as 0.091 $/kWh when 5% LPSP can be acceptable. 3) The policy of CR is unfavorable for the investors which leads to a higher investment cost. 4) Particle Swarm Optimization (PSO) performs better than genetic algorithm (GA) and Simulated Annealing method (SA) with a least LCOE. 5) both MOPSO and weighted sum approach (WSA) have a good performance to find the Pareto fronts and its hypervolume indicator (HV) is calculated.