A techno-economic multi-objective model for hybrid harmonic filter planning considering uncertainty in non-linear loads

In recent years, much work has been done on implementing a variety of algorithms in nonlinear programming software. In this paper, we analyze the performance of several state-of-the-art optimization codes on large-scale nonlinear optimization problems. Extensive numerical results are presented on different classes of problems, and features of each code that make it efficient or inefficient for each class are examined.Keywordsinterior-point methodslarge-scale optimizationnonlinear programming

Buildings contribute to more than 70% of overall U.S. electricity usage and greenhouse gas emissions. The future building control optimizes more connected devices and renewable energy equipment, which requires a rapid and accurate response. It also poses the challenge of solving non-linear problems with discrete variables in real time. In one study, we developed a novel optimization using quantum annealing for MPC of rooftop units in buildings. Our approach demonstrated significant improvements in computational speed from hours to seconds by considering day-ahead demand response signals. Our findings highlight the potential of quantum computing in solving large-scale non-linear discrete optimization problems for building energy systems. The other research presented a new framework for building-to-grid integration. We developed MPC for a commercial distribution grid for cost reduction. With similar solutions to traditional algorithms, quantum computing reduced computational time to less than 1.1%. We proved its potential to solve large-scale discrete optimization problems for urban energy systems

The growing penetration of renewable energy sources (RES) has boosted the necessity of cost-effective and flexible power system operations. Battery Energy Storage Systems (BESS) provide a real-world solution by improving grid reliability, facilitating flexible dispatch, and renewable integration. This paper models the Economic Dispatch (ED) problem with BESS integration, with an objective to minimize overall generation cost subject to operation constraints of thermal units and BESS. Two optimization methods are compared: (i) fmincon, a gradient-based constrained nonlinear solver, and (ii) the JAYA algorithm, a parameter-free metaheuristic that repeatedly pushes solutions towards best and away from worst. Simulation studies on a 6-bus system with six thermal units show that integration with BESS highly enhances operation flexibility and lowers costs. The total generation cost was reduced from Rs.23.74 million to Rs.21.21 million, corresponding to a saving of Rs.2.53 million (10.66%) using FMINCON whereas using JAYA algorithm the tota generation cost was reduced from Rs.23.68 million to Rs.20.83 million, corresponding to a saving of Rs.2.85 million (12.06%). The Jaya algorithm performs better consistently compared to fmincon in both cost minimization and the speed of convergence, showing its suitability for solving large-scale nonlinear power system optimization problems.

Genetic algorithms are able to search very large, variable complex spaces and locate the global optimum. However, there exist many difficulties in applying GA to large-scale nonlinear system optimization or "GA hard" problems. This paper presents an improved GA based on domain evolution model and fuzzy environment factor. Simulation study shows that it is a powerful search technique which can avoid premature convergence and locate the real global optimum. An example is given to show how this new algorithm can be successfully applied to solve large-scale industrial chemical separation process optimization problem.

Short-term scheduling and recipe optimization of blending processes

Conjugate gradient methods are widely acknowledged to be among the most efficient and robust methods for solving the large-scale unconstrained nonlinear optimization problems

A parallel method for solving the DC security constrained optimal power flow with demand uncertainties

This paper reports on a successful determination of the train travel schedule parameters for a rail system based on limited data, and thus provides a verification of the axiomatic safety-critical assessment process (ASCAP), a rail system simulator developed at the Center of Rail Safety-Critical Excellence at the University of Virginia. The train system considered is a corridor encompassing a territory of over 127 miles. It is divided into 37 train speed zones, with nine sidings. The only data available are the actual trip times of 171 trains dispatched over a period of 14 days. The problem of determining the 37 train-speed-zone average speeds and nine siding delay times was formulated as a constrained optimization problem. The cost to be minimized is the accumulated errors between the actual train trip times and the ASCAP simulated trip times resulting from a particular set of train-speed-zone average speeds and siding delay times. The constraints include allowable siding delays, permissible train-speed-zone average speeds and prohibition of southbound trains from entering the sidings. This large-scale non-linear optimization problem was then solved by a genetic algorithm developed by the present authors and referred to as the intelligent genetic algorithm. Simulation results demonstrate the effectiveness of this intelligent genetic algorithm.

Solids-based carbon dioxide (CO2)adsorption, where CO2 is adsorbed under high pressure/low temperature and desorbed at low pressure/high temperature, is a low energy consumption capture technology with high carbon uptake efficiency. Chemical looping combustion (CLC), where a solid oxygen carrier (OC) transports oxygen between two high-temperature reactors – a fuel and an air reactor –, is a low energy penalty, fuel-flexible technology with inherent CO2 capture for power/heat and syngas production. In this work, state-of-the-art equation-oriented (EO) mathematical models of a fixed-bed (FxB) contactor for CO2 adsorption and moving-bed (MB) reactors for gas-, and solid-fueled CLC processes are developed. The solid-fueled MB model is a multi-zonal model for the CLC of coal. Large-scale nonlinear optimization problems are solved for simultaneous optimization of design and operating conditions for the CLC processes. Further, a Bayesian parameter estimation and model building framework is proposed and used to quantify the parametric and model form uncertainty of CO2 adsorption isotherm and CLC OC reduction kinetic models based on experimental data. The model form discrepancy is described by a set of orthogonal functions which are naturally suitable and efficient for uncertainty propagation to the unit/process model in which the discrepancy-augmented models are implemented. By using the joint posterior distribution of the model and discrepancy function parameters and inserting discrepancy functions in the isotherm and kinetic models coupled with the EO unit/process reactor models, the effect of the uncertainty inherent to bench-scale adsorption/kinetic models on key process variables of the large-scale systems are evaluated.

An application of neural network modeling is described for generating hypotheses about the relationships between response properties of neurons and information processing in the auditory system. The goal is to study response properties that are useful for extracting sound localization information from directionally selective spectral filtering provided by the pinna. For studying sound localization based on spectral cues provided by the pinna, a feedforward neural network model with a guaranteed level of fault tolerance is introduced. Fault tolerance and uniform fault tolerance in a neural network are formally defined and a method is described to ensure that the estimated network exhibits fault tolerance. The problem of estimating weights for such a network is formulated as a large-scale nonlinear optimization problem. Numerical experiments indicate that solutions with uniform fault tolerance exist for the pattern recognition problem considered. Solutions derived by introducing fault tolerance constraints have better generalization properties than solutions obtained via unconstrained back-propagation.

In modern power systems, distributed generation (DG) plays an important role in energy losses reduction, but DG penetration in such networks is restricted by technical and investment limitations. Therefore, distribution network reconfiguration (DNR) and DG operation reduce power losses more efficiently than when only DG is utilized. Also, network reconfiguration mitigates improper DG placement in such systems which are subjected to high penetration of variable loads and DG. On the other hand, electrical energy demand is estimated by load forecasting methods and therefore is a time-variant and uncertain parameter. Moreover, DG power generation is uncertain and depends on variable load power. Consequently, models presented for the reconfiguration problem should be adequately robust against load and generation uncertainties. Thus, the present paper proposes an effective robust model for reconfiguring distribution networks with load and generation uncertainties. The proposed model is sufficiently simple for implementation and is accurate enough to find the optimal solutions in acceptable computational times. The main features of the proposed model are its high robustness against uncertain parameters, in which proposed configurations are not changed easily by small variations in DG power and load amounts, and its effectiveness, which means that uncertain parameters may change the obtained solutions.

With the embryonic form of the new power system emerging, the uncertainties of renewable energy and load have brought challenges to power generation and transmission planning, making it imperative to construct a coordinated planning model for "source-grid-load-storage". Meanwhile, in the environment of the power spot market, the extensive integration of renewable energy has increased the uncertainties and complexities of the system, imposing higher requirements on the coordination among "source-grid-load-storage". Under such circumstances, this paper constructs an expanded power generation and transmission planning model based on the power spot market. Considering the uncertainties of generation and load, a coordinated planning model for "source-grid-load- storage" in the power market environment is proposed. Through chance constraints, the adaptability of the planning scheme to the random uncertainties of generation and load is coordinated, thus effectively handling the two uncertainty factors. Finally, a provincial power grid in East China is taken for simulation analysis on the model to verify its scientific nature, effectiveness, and feasibility.

Scheduling short-term hydrothermal generation using predator prey optimization technique

As a novel logistics transportation mode, the passenger and freight integration model has garnered significant attention in recent years. This study focuses on collaborative delivery route planning for rural public buses and express delivery vehicles, exploring its application within the context of passenger and freight integration. For the pickup and delivery routing problem with time windows under demand uncertainty, we first developed a traditional model aimed at minimizing total costs. Subsequently, considering the integration of passenger and freight co-transportation with collaborative delivery between buses and courier vehicles, we formulated a model for location and collaborative routing planning under the passenger-freight co-transportation mode. This model addresses the pickup and delivery problem with time windows while accounting for demand uncertainty. Meanwhile, for the first model, a hybrid heuristic algorithm based on the moth–flame optimization (MFO) and single-parent genetic algorithm (SGA) is designed and implemented. The validation of the effectiveness of this algorithm is completed using standard arithmetic examples. For the second model, the rural route optimization method and the improved tabu search algorithm are developed, considering the pickup and delivery sequence. Finally, through empirical analysis in three townships in Hanzhong City, the study verifies the feasibility of the proposed models and methods, along with the advantages of the passenger and freight integration model in rural areas.

K-d tree (k-dimensional tree) is a space partitioning data structure for organizing points in a k-dimensional space. K-d tree, or Multidimensional Binary Search Tree is a useful data structure for several applications such as searches involving a multidimensional search key (e.g., Range Search and Nearest Neighbour Search). K-d trees are a special case of binary space partitioning trees.KNN Search is a searching algorithm with complexity O(N log N) {N= no. of data points}. This search algorithm is relatively better than brute force search {Complexity= O(n*k); where k=No. of neighbours searched, N=No. of Data Points in Kd tree} for dimensions N>>2D {N=No. of Points, D=Dimensionality of Tree}.Furthermore, Parallel KNN Search is much more efficient and performs better than KNN Search, as it harnesses parallel processing capabilities of computers and thus, results in better search time.This paper tests the time performance of KNN Search and Parallel KNN Search and compares them by plotting it on a 3D graph. A more comprehensive comparison is done by use of 2D graphs for each dimension(from 2 to 20).