System Optimization Problems Research Articles

A Review of Optimal Design for Large-Scale Micro-Irrigation Pipe Network Systems

Micro-irrigation pipe network systems are commonly utilized for water transmission and distribution in agricultural irrigation. They effectively transport and distribute water to crops, aiming to achieve water and energy conservation, increased yield, and improved quality. This paper presents a model for the scaled micro-irrigation pipeline network system and provides a comprehensive review of the fundamental concepts and practical applications of optimization techniques in the field of pipeline network design. This paper is divided into four main sections: Firstly, it covers the background and theoretical foundations of optimal design for scaled micro-irrigation pipeline network systems. Secondly, the paper presents an optimal design model specifically tailored for scaled micro-irrigation pipeline networks. And then, it discusses various optimization solution techniques employed for addressing the design challenges of scaled micro-irrigation pipeline networks, along with real-world case studies. Finally, this paper concludes with an outlook on the ongoing research and development efforts in the field of scaled micro-irrigation pipeline network systems. In addition, this paper establishes a fundamental model for optimizing pipeline networks, to achieve minimum safe operation and total cost reduction. It considers constraints such as pipeline pressure-bearing capacity, maximum flow rate, and diameter. The decision-making variables include pipeline diameter, length, internal roughness, node pressure, future demand, and valve placement. Additionally, this paper provides an extensive overview of deterministic methods and heuristic algorithms utilized in the optimal design of micro-irrigation pipeline networks. Finally, this paper presents future research directions for pipeline network optimization and explores the potential for algorithmic improvements, integration of machine learning techniques, and wider adoption of EPANET 2.0 software. These endeavors aim to lay a strong foundation for effectively solving complex and challenging optimization problems in micro-irrigation pipeline network systems in the future.

Tactical unit algorithm: A novel metaheuristic algorithm for optimal loading distribution of chillers in energy optimization

Metaheuristic algorithms have gained increasing popularity in practical problems of engineering optimization. Aim to address the optimal chiller loading (OCL) problem in parallel chiller systems and other energy system optimization problems more efficiently, with the aim of reducing energy consumption and carbon emissions, this paper proposes a novel metaheuristic algorithm named the Tactical Unit Algorithm (TUA). The search process of this algorithm can be divided into three stages: searchers action search phase, executors action execution phase, and assessors action evaluation phase. In this paper, we use MATLAB to conduct simulation tests of benchmark functions and OCL problems. To evaluate the performance of TUA, we conduct experiments using 24 benchmark functions and compare the results with those obtained from nine commonly used metaheuristic algorithms. The findings demonstrate that TUA exhibits more accurate search accuracy, faster convergence, and better stability. Furthermore, we apply TUA to optimal chillers loading in energy optimization, and the results of three classic case tests provide preliminary evidence of the feasibility of TUA in addressing the OCL problem.

Estimation of photovoltaic models using an enhanced Henry gas solubility optimization algorithm with first-order adaptive damping Berndt-Hall-Hall-Hausman method

A reliable methodology is essential for accurately estimating the parameters of PV models, enabling reliable performance evaluations, effective control studies, accurate analysis of partial shading effects, and optimal optimization of Photovoltaic (PV) systems. It ensures that the obtained parameters reflect the true characteristics of the PV system, leading to more accurate and reliable results in various applications. The existing literature extensively explores the utilization of powerful Metaheuristic Algorithms (MAs) to address the complex constrained optimization problem in PV systems and achieve optimal solutions. However, it is important to note that a significant portion of these MAs primarily concentrates on the development of methodologies, often overlooking the design of the objective function tailored for PV systems. This oversight has created a theoretical gap in this research domain, underscoring the necessity for additional exploration and investigation to address this limitation. To address the existing theoretical gap, this study focused on developing an objective function that accurately estimates the initial root parameters of Photovoltaic (PV) models. This objective function was designed by incorporating the first-order Berndt-Hall-Hall-Hausman (BHHH) numerical method, along with the non-linear damping parameter of the Levenberg-Marquardt technique (LM). By implementing this approach, the study aimed to significantly improve the precision and reliability of estimating the initial root parameters in PV models, effectively filling the theoretical void in this specific research area. Then in terms of methodology, the Enhanced Henry Gas Solubility Optimization (EHGSO) algorithm is combined with the Sine-Cosine mutualism phase of Symbiotic Organisms Search (SOS) for efficiently estimating the unknown parameters of PV models. The keystone of EHGSO in terms of methodology enhances exploration at the beginning of optimization and intensifies exploitation in later iterations. The proposed EHGSO methodology based on the adaptive damping BHHH technique (EHGSOAdBHHH) is tested on Single Diode (SD), and Double Diode (DD) PV models using actual experimental data. EHGSOAdBHHH exhibits outstanding accordance with attained experimental data compared with other algorithms, and its superiority is validated using several statistical criteria.

Optimization of Artificial Intelligence Algorithm based on Neural Network in Complex System

The purpose of this paper is to discuss the application of AI (Artificial Intelligence) algorithm with NN (Neural Network) in complex system optimization. Therefore, this paper takes the optimization of power system as the research object and designs a hybrid NN model. The model can effectively extract features from historical power data and predict future power demand and supply. At the same time, the NN model is optimized by genetic algorithm. The algorithm can efficiently search the optimal solution in a large solution space and has the ability to deal with multi-objective optimization problems. Finally, it is verified by experiments. By using test data sets to evaluate the model, it is found that the algorithm in this paper has high accuracy and applicability in dealing with power system optimization problems. At the same time, the model can effectively reduce the cost of power generation and improve the stability of the system. These achievements provide new ideas and methods for future complex system optimization, and provide useful reference for promoting the development and application of AI technology. In order to provide some guidance for practical application.

Distributed online optimization for heterogeneous linear multi-agent systems with coupled constraints

This paper studies a class of distributed online convex optimization problems for heterogeneous linear multi-agent systems. Agents in a network, knowing only their own outputs, need to minimize the time-varying costs through neighboring interaction subject to time-varying coupled inequality constraints. Based on the saddle-point technique, we design a continuous-time distributed controller which is shown to achieve constant regret bound and sublinear fit bound, matching those of the standard centralized online method. We further extend the control law to the event-triggered communication mechanism and show that the constant regret bound and sublinear fit bound are still achieved while reducing the communication frequency. Additionally, we study the situation of communication noise, i.e., the agent’s measurement of the relative states of its neighbors is disturbed by a noise. It is shown that, if the noise is not excessive, the regret and fit bounds are unaffected, which indicates the controller’s noise-tolerance capability to some extent. Finally, a numerical simulation is provided to support the theoretical conclusions.

Measurements-based constrained control optimization in presence of uncertainties with application to the driver commands for high-speed trains

The railway world is undergoing major changes. The advent of new technologies allows us to rethink the train system and face new challenges, but one must not forget all the ecological constraints that are now accentuated by the increase in energy costs. This paper focuses on the optimization of the driver commands to limit the energy consumption of the trains under punctuality and security constraints. This problem falls within the framework of control optimization problems for nonlinear dynamic mechanical systems in the presence of constraints and uncertainties. A four-step approach is then proposed in this paper to solve this problem: (1) the introduction of simplified and fast-to-evaluate models to model the nonlinear dynamic behavior of the train and its energy consumption; (2) the identification in a Bayesian formalism of the parameters on which these models depend from on-track measurements on commercial trains; (3) the reformulation of the optimization problem so that it integrates the uncertainties related to an imperfect knowledge of these estimated parameters; (4) the resolution of the optimization problem using evolutionary algorithms. The main specificity of this work lies in the fact that not only the objective function to be minimized, here the energy consumed by the train, is impacted by the uncertainties, but also the admissibility constraints of the solution, here punctuality and operating safety. The integration of the uncertainties in the search for the control function is thus not trivial and requires several original adaptations in order to make the final optimization problem well posed.

An Advanced High Dimensional Power System of Optimization Problems Algorithm

New Evolutionary Algorithm-based for High-Dimensional Power System Optimization Problems and Modern power system optimization problems face growing challenges as there are numerous decision variables and unbiased functions. Multi-objective evolutionary algorithms (MOEAs) basically are popularly applied for solving and dealing with such complicated problems in substantial power systems that have several objective functions. However, customary MOEAs works appropriately when the number of objective features comes to be less than three. Generally, the effectiveness of MOEAs begins to decrease apparently with an increase in the number of unbiased functions. This study is devoted to applying an authentic gradable evolutionary algorithm for the purpose of solving power system optimization issues which manifest numerous objective functions. In particular, this seeks an algorithm based upon NSGA-II algorithm dedicated for an efficient solution of multi-objective optimization problems. Here, we attempt to modify NSGA-II algorithm to effectively solve many target optimization problems. A novel effective grouping method is applied in the proposed algorithm in similar to the non-dominant grouping method of NSGA-II algorithm to accelerate decision convergence action in POF. The recommended algorithm is compared with modern numerous objective optimization algorithms following three test problems. The findings reveal that as the number of unbiased functions is noticeably large, the algorithm that we propose is significantly superior to other algorithms.

The Optimal Control Algorithm of the Integrated Energy System Based on Demand Response

As an important embodiment of energy diversification demand, comprehensive energy has been widely used. Aiming at the problem of system optimization for the dynamic characteristics of multi-energy networks in the integrated energy system, this paper established the integrated energy system optimization control model. It optimized the next-day system load curve based on the integrated demand response and verified the effectiveness of the algorithm in this paper. The results show that the algorithm model in this paper can improve the flexibility and economy of the system operation. It can ensure the safety of the integrated energy system operation. It can also effectively improve the economy of the system and reduce the system operation cost and promote the consumption of clean energy, which shows the effectiveness of the optimization control algorithm in this paper.

Black Widow Optimization for Power System Load Frequency Control: A Comparative Study

This research paper mainly engrossed on developing a suitable novel tuning methodology named Black Widow Optimization (BWO) Algorithm for power system optimization problems. Load Frequency Control (LFC) and Automatic Voltage Regulator (AVR), two of the most important control systems in the power system arena, are employed as test systems to assess the efficiency of the suggested BWO approach. Various analyses, such as transient analysis, are employed to evaluate the efficiency of the suggested BWO approach in LFC and AVR systems. robustness analysis and convergence analysis. The significant performance measures of test system such as output power of each generating unit, maximum peak, settling time, steady state error, rise time and voltage/frequency deviations of the frequency and voltage responses are considered for the comparison purposes. The comparative analysis clearly demonstrates that the proposed novel tuning methodology provides better transient performances subjected to Step Load Perturbations (SLP), Variable Load Perturbations (VLP), and robustness performances under a wide range of load and parameter changes ranging from -50% to +50%. The convergence analysis of BWO algorithm also performed in LFC and AVR test systems and results better convergence characteristics with minimal convergence time as well as least fitness value is obtained with the BWO algorithm.

AI-Based Q-Learning Approach for Performance Optimization in MIMO-NOMA Wireless Communication Systems

In this paper, we investigate the performance enhancement of Multiple Input, Multiple Output, and Non-Orthogonal Multiple Access (MIMO-NOMA) wireless communication systems using an Artificial Intelligence (AI) based Q-Learning reinforcement learning approach. The primary challenge addressed is the optimization of power allocation in a MIMO-NOMA system, a complex task given the non-convex nature of the problem. Our proposed Q-Learning approach adaptively adjusts power allocation strategy for proximal and distant users, optimizing the trade-off between various conflicting metrics and significantly improving the system’s performance. Compared to traditional power allocation strategies, our approach showed superior performance across three principal parameters: spectral efficiency, achievable sum rate, and energy efficiency. Specifically, our methodology achieved approximately a 140% increase in the achievable sum rate and about 93% improvement in energy efficiency at a transmitted power of 20 dB while also enhancing spectral efficiency by approximately 88.6% at 30 dB transmitted Power. These results underscore the potential of reinforcement learning techniques, particularly Q-Learning, as practical solutions for complex optimization problems in wireless communication systems. Future research may investigate the inclusion of enhanced channel simulations and network limitations into the machine learning framework to assess the feasibility and resilience of such intelligent approaches.

Distributed Adaptive Optimization Algorithm for Fractional High-Order Multiagent Systems Based on Event-Triggered Strategy and Input Quantization

This paper investigates the distributed optimization problem (DOP) for fractional high-order nonstrict-feedback multiagent systems (MASs) where each agent is multiple-input–multiple-output (MIMO) dynamic and contains uncertain dynamics. Based on the penalty-function method, the consensus constraint is eliminated and the global objective function is reconstructed. Different from the existing literatures, where the DOPs are addressed for linear MASs, this paper deals with the DOP through using radial basis function neural networks (RBFNNs) to approximate the unknown nonlinear functions for high-order MASs. To reduce transmitting and computational costs, event-triggered scheme and quantized control technology are combined to propose an adaptive backstepping neural network (NN) control protocol. By applying the Lyapunov stability theory, the optimal consensus error is proved to be bounded and all signals remain semi-global uniformly ultimately bounded. Simulation shows that all agents reach consensus and errors between agents’ outputs and the optimal solution is close to zero with low computational costs.

On the сontrollability, observability, and optimization of discrete nonstationary linear Volterra systems

In this article, we study discrete nonstationary linear dynamic systems of Volterra type. An essential feature of such kind of systems is that their current states depend on the previous states of this system. The formula Cauchy, which gives us the solution of linear Volterra systems with the control inputs, is obtained. The necessary and sufficient conditions of the pointwise controllability, pointwise output controllability, and observability are proven. Also the linear-quadratic optimization problem for the nonstationary Volterra control systems is studied.

Enhanced coati optimization algorithm-based optimal power flow including renewable energy uncertainties and electric vehicles

Power systems now face new issues due to incorporating electric vehicles (EVs) and renewable energy resources (RERs). This paper proposes a novel Enhanced Coati Optimization Algorithm (ECOA) for obtaining the optimal solution of the probabilistic optimal power flow (POPF) problems. The ECOA is a metaheuristic optimization algorithm that is robust and efficient for solving complex problems. It is used to tackle the OPF problem, which considers the stochastic characteristics of RERs. Moreover, EVs are included in the presented power systems in this paper. The novel approach is tested and verified on the IEEE-57 and IEEE-118 networks. The effectiveness of the proposed method is demonstrated by making a comparison with other metaheuristic-based methods. To obtain a practical study, real data of wind speed, solar irradiance, and electric vehicles profile are incorporated in the dynamic analyses. The simulation results show that the ECOA is robust and efficient for solving the OPF problem. It can also improve the performance of power systems with RESs and EVs. The findings of this research demonstrate that the suggested approach is promising for power system optimization problems, including RERs and EVs.

Sparse structure preserving finite element model updating problem for gyroscopic systems

An important yet difficult aspect of the finite element model updating problem is to preserve the original inherent structures in the updated model. The analytical matrices resulting from the discretization of distributed parameter systems using finite element techniques are often symmetric and sparse. In this paper, an iterative method is proposed for solving the constrained optimization problem (COP) of gyroscopic systems, that is, finding the optimal updated matrices M̂,Ĝ and K̂ that satisfy the constraints of the characteristic equation, symmetry (or skew-symmetry) and sparsity. The proposed method is proved to be convergent and numerical examples verify the effectiveness of the method.

Deep Reinforcement Learning for Dynamic Opportunistic Maintenance of Multi-Component Systems With Load Sharing

Opportunistic maintenance (OM), which shows its superiority on complex multi-component systems by integrating the maintenance activities of multiple components to reduce the maintenance cost, has been widely studied over the past decade. To our knowledge, most of the existing OM works are developed based on fixed maintenance thresholds without fully utilizing the health state of the multi-component system. This article presents an OM optimization problem of multi-component systems with load sharing, solved by a modified proximal policy optimization approach based on deep reinforcement learning algorithm. The load sharing effect is reflected in the hazard rate function, which further changes the failure probability of the components. Meanwhile, the health states can be recovered by executing imperfect maintenance and corrective maintenance. The optimization problem is formulated as an infinite-horizon MDP with mixed discrete and continuous state and action space to maximize the total discounted reward, taking into account the system reliability and the maintenance cost. The difficulty caused by the mixed action space is solved by designing a parameterized action space structure and multi-task reinforcement learning framework. The effectiveness of the proposed algorithm is tested on a four-component system and a real-world scenario configured with the high-pressure feedwater heater system in the nuclear power plant. The results show that the performance of the algorithm is stable when facing large-scale problems. The algorithm proposed in this study also contributes to the imperfect maintenance optimization with state-of-the-art optimization techniques.

Distributed Optimization for Second-Order Discrete-Time Multiagent Systems With Set Constraints.

The optimization problem of second-order discrete-time multiagent systems with set constraints is studied in this article. In particular, the involved agents cooperatively search an optimal solution of a global objective function summed by multiple local ones within the intersection of multiple constrained sets. We also consider that each pair of local objective function and constrained set is exclusively accessible to the respective agent, and each agent just interacts with its local neighbors. By borrowing from the consensus idea, a projection-based distributed optimization algorithm resorting to an auxiliary dynamics is first proposed without interacting the gradient information of local objective functions. Next, by considering the local objective functions being strongly convex, selection criteria of step size and algorithm parameter are built such that the unique solution to the concerned optimization problem is obtained. Moreover, by fixing a unit step size, it is also shown that the optimization result can be relaxed to the case with just convex local objective functions given a properly chosen algorithm parameter. Finally, practical and numerical examples are taken to verify the proposed optimization results.

Mobility-aware caching strategy in an SDN-based cooperative caching network

The upsurge of mobile multimedia traffic puts a heavy burden on the cellular network, and wireless caching has emerged as a powerful technique to overcome the backhaul bottleneck and alleviate the network burden. However, most previous works ignored user mobility, thus not reaping the caching gain from user mobility and having limited practical applications. In this paper, a mobility-aware caching strategy for the software-defined network (SDN)-based network is studied. Firstly, since typical mobile user (MU) has multiple opportunities to connect with the nearby MUs and Small-base stations (SBSs), the contact times between MUs as well as between MU and SBSs are derived as the Poisson distribution and Gamma distribution. Secondly, we propose a two-tier cooperative caching strategy, where SBSs cache the rateless Fountain code encoded video blocks probabilistically and nonrepeatedly while MUs just store the whole encoded video received last time. The corresponding four-stage transmission process is analyzed, where the key intermediate step is the derivation of the service and the failing probabilities of each transmission manner. Finally, we derive the successful offloading rate and average data offloading ratio (ADOR) as performance metrics. A system optimization problem based on ADOR is formulated, and two solutions are proposed, namely, derivative-based solution (DB-Solution) and long-tail distribution approximation (LTD-Approximation). Simulation results demonstrate that the effectiveness of LTD-Approximation is similar to the DB-Solution, and the proposed caching strategy can achieve quasi-optimal performance compared with other contrast schemes.

Reconfiguration of Distribution Network based DG and Capacitors using Grey Wolf Algorithm for Power Loss Reduction

Network reconfiguration is one of the methods used for power loss reduction in distribution network by altering the close/open position of Tie/Sectionalizing switches without interruption of power to all loads. The other methods used are placing of distribution generators and connecting capacitors. The best results are obtained when all the three are used simultaneously using grey wolf algorithm which is a recent evolutionary algorithm which gives the place of connection and values of capacitor and distribution generators along with sectionalizing switches which are to be opened. This algorithm was implemented for a standard 33 and 69 bus distribution system in a MATLAB environment for comparing with other algorithms with constraints. This algorithm has good exploration (new search) and exploitation (thorough local search) potential for application in many power system optimization problems.

Development, validation, and testing of advanced mathematical models for the optimization of BESS operation

Current decarbonization strategies are driven by the fast-paced diffusion of non-programmable renewable energy sources (NP-RESs), mainly through solar and wind power generation. Energy storage technologies are emerging as key solutions for coping with the variability and low-inertia characteristics of NP-RESs. Particularly, battery energy storage systems (BESS) are diffusing more widely for both behind-the-meter (BTM) and utility-scale applications. In this context, we still lack a shared solution on how to proceed from the on-field data collected about the performance of BESS to reliable and fast mathematical formulations for operational optimization. This study provides a validated modeling framework that can be exploited during or after BESS commissioning to (i) identify and derive the useful parameters to characterize BESS performances, (ii) formalize them in a mathematical formulation while being aware of its specific trade-off between accuracy and computational effort, and (iii) exploit the selected BESS model within a multi-energy system optimization problem. We discuss three different modeling approaches that we developed for optimizing BESS operation, with each providing a different balance between modeling accuracy and computational effort. These three mathematical models were validated against a numerical simulation model based on on-field performance data, and they were eventually tested on a reference case study. The results indicate that it is possible to restrict the average error in estimating BESS efficiency while simultaneously limiting the computational effort of the model. Regarding the operation of BESS, the conducted simulations demonstrate that an approximate BESS model may result in an overestimation of the expected revenues.

Formation of Exact Co-ordination Equations with Proper Selection of Design Variables for Active and Reactive Power Scheduling

The Design variables in objective function must be an independent variables for best solutions in Pwer system Optimization Problems, whatever may be the mathamatical and computation Techniques. Selection of entire ingected Generator (Source) powers in to the network are not independent variables since the naetwork loss is present in the generator powers which depends on how the generators share the total system load. The work presents the revision of fundamental coordination equations to prove the concept.The genarator contributions to the system load are considered as design(independent variables).An example is given to ilstrate the application of the prposed derived equqtions.