Adaptive convex relaxations for Gas Pipeline Network Optimization

Optimization of the multi-level steam production and supply in industrial parks

In model predictive control, the control action is found at each sampling time by solving an online optimization problem. Computationally, this step is very demanding, especially if compared to the evaluation of traditional control laws. This has limited the application of model predictive control to systems with slow dynamics for many years. Recently, several methods have been proposed in the literature which promise a substantial reduction of the computation time by either running the computation in parallel (distributed model predictive control) or exploiting the problem structure (fast model predictive control). A combination of these methods has not yet been considered in the literature. To achieve this goal, different optimization techniques need to be employed at once. The order of how these methods are applied matters. This paper considers fast distributed model predictive control combining the alternating direction method of multipliers (ADMM), the interior point method (IPM) and the Riccati iteration for a particular class of multi-agent systems for which the order of the methods can be arbitrarily changed. This leads to two different solver schemes where a trade-off arises between computation time and number of communications required to reach consensus. A simplified problem involving the formation control of a fleet of vehicles is considered at the end.

Computer-aided modeling and mathematical optimization of energy systems are essential for improving operational efficiency and achieving emission reductions, particularly for steam turbine systems with part-load-dependent efficiency characteristics. Mixed-Integer Linear Programming (MILP) is the state of the art, due to its short computational times and reliable convergence. However, its simplifications often reduce model accuracy. Mixed-Integer Nonlinear Programming (MINLP) offers high accuracy but faces long computational times and potential convergence issues. Recent advancements in Mixed-Integer Quadratically Constrained Programming (MIQCP) offer a promising approach for more accurate energy system modeling by enabling quadratic and bilinear representations while avoiding the full complexity of nonlinear programs. This study compares the optimization methods MILP, MINLP and MIQCP for the operational optimization of a steam turbine system. The parameterization of the models is based on hourly measurement data of two real-world steam turbines. Key evaluation criteria include accuracy, computational time, implementation complexity and the deviation in the calculated optimum. The results show that MIQCP improves accuracy compared with MILP while requiring lower computational time than MINLP. Overall, the results demonstrate that MIQCP provides a suitable compromise between model accuracy and computational efficiency for the operational optimization of steam turbine systems.

Hybrid propulsion is considered a reliable alternative to solely mechanical or electrical propulsion for enhanced ship energy performance. Nevertheless, an increased number of components and interconnections results in more complex ship design problems. The automotive and aviation industries already examine new designs on predefined driving and flying cycles. However, new ships are still assessed on one design point with the regulated Energy Efficiency Design Index (EEDI). Its limited consideration of calm water conditions and installed rated power is characterised as insufficient, if not dangerous. A design methodology that accounts for operational and environmental uncertainty is lacking. This paper proposes a design optimisation framework for the topology selection and sizing of hybrid propulsion systems integrating probability distributions of actual sailing profiles from continuous monitoring. The methodology is demonstrated on the ‘Holland’ class ocean patrol vessels of the Royal Netherlands Navy. Its multi-objective consideration examines a wide design space from an environmental, financial, and technical perspective, solving the mixed-integer nonlinear programming (MINLP) problem with a multi-starting scheme that combines a genetic algorithm and interior point method. The low computational cost is achieved by integrating a state-of-the-art digital twin approach leveraging data-driven and first-principle modelling. The results demonstrate feasible improvements of approximately 4 % for carbon intensity and 11 % for operational expenditure by increasing the size of the electrical motors. The exact configuration and percentage improvement are sensitive to actual operational and environmental conditions, while calm water conditions tend to overestimate savings. Consequently, the use of actual sailing profiles is recommended for more accurate life-cycle predictions. • Use of discretised probability distributions of actual sailing profiles of sister ships. • A framework that considers environmental, financial and technical objectives. • A modelling approach that allows for extensive design space exploration. • A solver combining a genetic algorithm and interior point method in a multi-start scheme. • Calm water and typical design assumptions misleading in life-cycle assessments.

Pipelines in critical engineered facilities, such as petrochemical and power plants, conduct important roles of fire extinguishing, cooling, and related essential tasks. Therefore, failure of a pipeline system can cause catastrophic disaster, which may include economic loss or even human casualty. Optimal sensor placement is required to detect and assess damage so that the optimal amount of resources is deployed and damage is minimized. This paper presents a novel methodology to determine the optimal location of sensors in a pipeline network for real-time monitoring. First, a lumped model of a small-scale pipeline network is built to simulate the behavior of working fluid. By propagating the inherent variability of hydraulic parameters in the simulation model, uncertainty in the behavior of the working fluid is evaluated. Sensor measurement error is also incorporated. Second, predefined damage scenarios are implemented in the simulation model and estimated through a damage classification algorithm using acquired data from the sensor network. Third, probabilistic detectability is measured as a performance metric of the sensor network. Finally, a detectability-based optimization problem is formulated as a mixed integer non-linear programming problem. An Adam-mutated genetic algorithm (AMGA) is proposed to solve the problem. The Adam-optimizer is incorporated as a mutation operator of the genetic algorithm to increase the capacity of the algorithm to escape from the local minimum. The performance of the AMGA is compared with that of the standard genetic algorithm. A case study using a pipeline system is presented to evaluate the performance of the proposed sensor network design methodology.

With the continuous development of diagnosis, prediction and systems management technologies for complex pipeline systems and driven by the context of Industry 4.0, digital twin technology has become a research hotspot in the field of smart manufacturing and intelligent operation and maintenance of pipeline systems. Digital twin can be defined as an adaptive model of complex physical systems. The prospect of digital twin and its impact on society is brought closer to reality with recent advances in multiphysics simulation, artificial intelligence, big data cybernetics, data processing and management tools. With a case study on Sino-Myanmar pipelines, the author uses artificial intelligence models on the basis of digital twin technology to predict the pipeline transmission demand to make sure the smooth operation of pipelines with large topographic fluctuations and geological complexity. The objective is to achieve intelligent operation such as real-time autonomous optimization of pipeline operation and control of safety warning, thus realizing data analysis and intelligent diagnosis, as well as providing a reference for the application of demand prediction of complex pipeline networks and system health management in digital twin technology.

Optimization problems in natural gas transportation systems: A state-of-the-art review

In this paper, a Mixed-integer non linear programming (MINLP) model for a multi-facility location problem in the presence of a probabilistic line barrier is presented in which the starting point of the line barrier uniformly distributed. It is supposed that the distances metric is rectilinear. The objective function minimizes the summation of the weighted expected rectilinear barrier distances between the n new facilities and the m existing facilities and also between the new facilities. To show the applicability of the model, a numerical example is provided. It is worth noting that the global optimal solution is found.

In recent decades, due to the limitations of resources and equipment in the construction of engineering projects, the optimal use of existing facilities for reducing the overall costs of the projects has been of great importance. Due to the significant advances in optimization techniques, the application of these methods in engineering sciences, especially civil and structural engineering, has received more attention than before. In the last years, various optimization methods and algorithms have been used for optimization purposes, including metaheuristic algorithms. In this paper, the optimum design of truss structures is considered utilizing Atomic Orbital Search (AOS) as one of the recently developed metaheuristic algorithms. The applicability of this technique in the weight optimization of complex truss structures is in perspective. Four of the benchmark problems in the truss optimization field, such as 10-bar, 25-bar, 52-bar, and 160-bar truss structures, are used as design examples. A simple penalty approach is implemented in AOS as a constraint handling scheme, while a comprehensive statistical analysis is also conducted through multiple optimization runs. The results of the AOS are compared to the results of other methods from the literature, while the AOS demonstrates a good level of weight reduction capability in dealing with these complex design problems.KeywordsTruss structureAtomic orbital searchMetaheuristic algorithmOptimization first section

Power quality management based environmental optimal power flow (OPF) is a vital re‐ search area for power system operation and control, this type of problem is complex with non-linear constraints where many conflicting objectives are considered like fuel cost, gaz emission, real power loss, voltage deviation, and voltage stability[1-2]. The solution of this multi objective problem becomes more complex and important when flexible ac transmis‐ sion systems (FACTS) devices and renewable energy are considered. In the literature a wide variety of optimization techniques have been applied, a number of approaches have been developed for solving the standard optimal power flow problem using mathematical pro‐ gramming, lambda iteration method, gradient method, linear programming, quadratic pro‐ gramming and interior point method [3-4-5]. However all these developed techniques rely on the form of the objective function and fail to find the near global optimal solution, au‐ thors in [6-7] provide a valuable introduction and surveys the first optimization category based conventional optimization methods.

Interior point methods in the year 2025

An interior point method (IPM) for solving the optimal power flow (OPF) problem is presented. The IPM uses a combination of the predictor corrector, multiple predictor corrector and multiple centrality correction methods (all belong to the family of higher order interior point methods). The proposed IPM uses the best properties of each method to obtain a more robust IPM with faster convergence characteristics. The active power loss minimisation, minimum load shedding and maximum loadability problems are formulated as an OPF problem and solved with the proposed methodology. The IEEE 30, 57, 118, and 300 bus systems, and two realistic power systems, a 464 bus corresponding to the interconnected Peruvian system, and a 2256 bus corresponding to part (South–Southeast) of the interconnected Brazilian system were tested successfully. Results have indicated that good convergence performance is obtained and the computational time is small.

Green vehicle routing and scheduling problem with heterogeneous fleet including reverse logistics in the form of collecting returned goods

Due to the enormous energy consumption and the wide geographic distribution, Electrical Vehicle (EV) charging stations are believed to have great potential in Emergency Demand Response (EDR) participation. However, EDR limits the electricity drawn from the power grid by the charging station, and can pose threats to satisfying EVs’ charging demand. In this paper, in order to complement the charging station’s energy supply to meet the dynamic EV charging demand, we formulate an online EV charging scheduling problem under EDR as a non-linear mixed-integer program, and propose a novel polynomial-time online algorithm and auction mechanism to jointly incentivize EVs with energy to sell their energy and utilize the charging station’s local generator to produce energy. Our approach conducts an auction in each single round based on a primal-dual method and ties these auctions over time to optimize the system’s long-term social cost, while accommodating the local generator’ on/off-state control, each EV bidder’s cumulative energy budget constraint, and the power grid’s EDR energy cap. Our approach achieves the economic properties of truthfulness, individual rationality, and computational efficiency simultaneously for each auction, and a parameterized-constant competitive ratio for the long-term social cost. By rigorous theoretical analysis and trace-driven experimental studies, the results exhibit that our approach outperforms multiple alternative algorithms regarding the social cost, attains the economic properties, and also executes efficiently in practice.

The use of the computer program MATLAB is prominent in many studies that simulate many industrial systems. The current simulation aims to build a suitable simulation model representing the Two-phase Hybrid Stepping Motor (2Ph-HSM). This type of motor is employed in a specific application to produce a force called automatic grinding force. To control the force, motor speed, and location, we need to add control systems, so two methods have been proposed, one of which is traditional, namely proportional, integral, and derivative (PID) control and the other is intelligent, called Gray Wolf Optimization (GWO). The current work also aims to use traditional control algorithms and advanced optimization algorithms that were chosen for their ease of control and possibility of use in many industrial applications. By setting appropriate specifications for the simulation model and after conducting prescribed tests that simulate different applications of the motor’s work within electrical systems, the results demonstrated good motor performance, better response, and high accuracy, in addition to speed. The goal is to design and tune a proportional, integral, and derivative (PID) controller by gray wolf optimization (GWO) using the transfer function (TF) of a 2Ph-HSM. To adjust the parameters of conventional controllers using advanced optimization, a suitable mechanism and technique were selected from advanced optimization techniques, where the gray wolf technique algorithm was chosen as an optimization technique and Integrated Time Absolute Error (ITAE) to adjust the parameters of conventional PID controller.