Optimal Sequence Of Operations Research Articles

Two-Stage Optimization Model Based on Neo4j-Dueling Deep Q Network

To alleviate the power flow congestion in active distribution networks (ADNs), this paper proposes a two-stage load transfer optimization model based on Neo4j-Dueling DQN. First, the Neo4j graph model was established as the training environment for Dueling DQN. Meanwhile, the power supply paths from the congestion point to the power source point were obtained using the Cypher language built into Neo4j, forming a load transfer space that served as the action space. Secondly, based on various constraints in the load transfer process, a reward and penalty function was formulated to establish the Dueling DQN training model. Finally, according to the ε−greedy action selection strategy, actions were selected from the action space and interacted with the Neo4j environment, resulting in the optimal load transfer operation sequence. In this paper, Python was used as the programming language, TensorFlow open-source software library was used to form a deep reinforcement network, and Py2neo toolkit was used to complete the linkage between the python platform and Neo4j. We conducted experiments on a real 79-node system, using three power flow congestion scenarios for validation. Under the three power flow congestion scenarios, the time required to obtain the results was 2.87 s, 4.37 s and 3.45 s, respectively. For scenario 1 before and after load transfer, the line loss, voltage deviation and line load rate were reduced by about 56.0%, 76.0% and 55.7%, respectively. For scenario 2 before and after load transfer, the line loss, voltage deviation and line load rate were reduced by 41.7%, 72.9% and 56.7%, respectively. For scenario 3 before and after load transfer, the line loss, voltage deviation and line load rate were reduced by 13.6%, 47.1% and 37.7%, respectively. The experimental results show that the trained model can quickly and accurately derive the optimal load transfer operation sequence under different power flow congestion conditions, thereby validating the effectiveness of the proposed model.

Hydrogen storage planning robust to year‐round net load fluctuation

AbstractLong‐term hydrogen storage systems are considered a solution to the long‐term supply imbalance caused by different seasonal characteristics in renewable energy output and load. However, most existing planning models assume the whole year‐round net load series is known in advance and the optimal operation sequence of long‐term storage is derived accordingly. This assumption underestimates the necessary long‐term storage capacity because of the difficulty of accurate fine‐grained long‐term forecasting. This work proposes a long‐term hydrogen storage planning framework that is robust to year‐round net load fluctuation. The daily average component from the historical net load series is first derived to formulate the long‐term operation scenario set. On this basis, a robust planning model is developed where the trend of long‐term storage operation is forced to be identical for all long‐term operation scenarios and not to respond to one specific scenario with full information of the net load sequence. The results show that the planning results given by the proposed framework are more robust in real‐time operation with less load shedding.

Energy-efficient path planning for a multi-load automated guided vehicle executing multiple transport tasks in a manufacturing workshop environment.

Automated guided vehicles (AGVs) are typical intelligent logistics equipment, and path planning plays a significant role in the efficient use of AGVs. To better utilize multi-load AGVs and enhance the sustainability of the logistics process, an energy-efficient path planning model is formulated for a multi-load AGV executing multiple transport tasks in a manufacturing workshop environment, with transport distance and energy consumption (EC) serving as optimization objectives. Furthermore, a two-stage approach is proposed to solve it. In the first stage, the optimal energy-efficient paths connecting any two different nodes are acquired based on the workshop transport network expressed as a topological map. Afterward, the non-dominated sorting genetic algorithm-II is adopted in the second stage to determine the optimal execution sequence of pickup and delivery operations related to the assigned transport tasks, as well as to select the optimal path from the first stage's output information to execute each operation simultaneously. Moreover, the experimental study validates the energy-saving effect of the established model and the effectiveness of the solution method, and the factors affecting the multi-load AGV EC are analyzed.

Optimal nudging for cognitively bounded agents: A framework for modeling, predicting, and controlling the effects of choice architectures.

People's decisions often deviate from classical notions of rationality, incurring costs to themselves and society. One way to reduce the costs of poor decisions is to redesign the decision problems people face to encourage better choices. While often subtle, these nudges can have dramatic effects on behavior and are increasingly popular in public policy, health care, and marketing. Although nudges are often designed with psychological theories in mind, they are typically not formalized in computational terms and their effects can be hard to predict. As a result, designing nudges can be difficult and time-consuming. To address this challenge, we propose a computational framework for understanding and predicting the effects of nudges. Our approach builds on recent work modeling human decision making as adaptive use of limited cognitive resources, an approach called resource-rational analysis. In our framework, nudges change the metalevel problem the agent faces-that is, the problem of how to make a decision. This changes the optimal sequence of cognitive operations an agent should execute, which in turn influences their behavior. We show that models based on this framework can account for known effects of nudges based on default options, suggested alternatives, and information highlighting. In each case, we validate the model's predictions in an experimental process-tracing paradigm. We then show how the framework can be used to automatically construct optimal nudges, and demonstrate that these nudges improve people's decisions more than intuitive heuristic approaches. Overall, our results show that resource-rational analysis is a promising framework for formally characterizing and constructing nudges. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Метод нахождения динамических приоритетов грузовых операций для оптимизации работы самоходных подвижных единиц в железнодорожных промышленных транспортно-технологических системах

The application of fuzzy logic is considered in optimizing the sequence of operations that involve the use of self-propelled units in industrial railway transportation. The consequences of using constant values calculated analytically as operation priorities have been analyzed. The type of queue discipline and the specifics of constraints on the stay of requests in the queue, characteristic of railway industrial transport and technological systems, are described. The advantages and disadvantages of existing methods for queuing freight operations specified in the daily work plan in railway industrial transport and technological systems are investigated. Limitations arising from the use of classical scheduling theory methods have been identified. Finding the optimal sequence of operations in railway industrial transport and technological systems involves simultaneous movement planning for multiple mobile units, which is not considered in classical graph theory. The classical problem of finding the shortest path and its known solving algorithms only work with static graphs, while railway industrial transport and technological systems are characterized by rapid changes in the graph's state. The problems caused by the dynamic nature of the graph's state in railway industrial transport and technological systems have been analyzed. The application of fuzzy logic methods will enable the construction of an optimal sequence of operations based on incomplete and imprecise information and address several operational planning tasks for freight operations without precise calculations. The expected result of applying the method for determining dynamic operation priorities implemented in the neuro-fuzzy module is an increase in the adaptability of operational planning for freight operations and a reduction in the time of engagement and the required number of self-propelled mobile units.

Automatic shape adaptation scheme planning for CAD models in direct modeling

Designing a new CAD model by adapting a retrieved candidate model offers several advantages; however, shape adaptation is often a tough job. To facilitate shape adaptation, direct modeling (technology) provides a very flexible and intuitive approach. However, based on new design requirements, the research on automatic and effective methods to determine the geometries on the candidate model that need adapting and how to adapt these geometries has rarely been reported till date. The shape adaptation based on direct modeling is often dependent on manual processing and thus it is usually a tedious process. To account for this, a novel automatic shape adaptation scheme planning approach is proposed in this study. The candidate model is usually retrieved using a more shape-simple query model that indicates the overall shape meeting the main functional requirements of a final product (i.e., new primary shape requirements); therefore, this study focuses on the automatic adaptation of the shape of the candidate model to that of the query model to accelerate its entire adaptation progress. First, to describe the shape requirements indicated by a CAD model, an attributed face-layout graph (AFLG) is presented. Further, by comparing the candidate model’s AFLG to that of the query model, the geometries (i.e., faces) on the candidate model requiring adaptation can be determined. Next, integrating with a geometric detail reuse strategy and a novel incremental adapting method on the presented graph, an improved particle swarm optimization method is developed to determine an optimized shape adaptation scheme (i.e., an optimized direct modeling operation sequence) based on the determined geometries. Finally, by applying the above-mentioned scheme, the face layout and shape parameters are smoothly transferred from the query model to the candidate model as shape requirements, and the shape of the candidate model gets automatically adapted to the query model while its geometric details are reused adaptively. Besides, a prototype system is also implemented, and the experimental results are analyzed to verify the effectiveness and characteristics of the proposed approach. • Determining geometrics with different shape requirements by comparing new descriptors. • Generating optimized operation sequence based on a novel graph incremental adapting. • Reusing geometric details adaptively based on topology change prediction.

Solving the Container Relocation Problem by Using a Metaheuristic Genetic Algorithm

Maritime transport is the backbone of international trade of goods. Therefore, seaports are of great importance for maritime transport. Container transport plays an important role in maritime transport and is increasing year by year. Containers transported to a container terminal are stored in container yards side by side and on top of each other, forming blocks. If a container that is not on top of the block has to be retrieved, the containers that are above the required container must be relocated before the required container is retrieved. These additional container relocations, which block the retrieval of the required container, slow down the entire retrieval process. The container relocation problem, also known as the block relocation problem, is an optimization problem that involves finding an optimal sequence of operations for retrieving blocks (containers) from a container yard in a given order, minimizing additional relocations of blocking containers. In this paper, the focus is on the two-dimensional, static, offline and the restricted container relocation problem of real-size yard container bays. A new method for resolving the container relocation problem that uses a genetic algorithm is proposed to minimize the number of relocations within the bay. The method is evaluated on well-known test instances, and the obtained results are compared with the results of various relevant models for resolving the container relocation problem. The results show that the proposed method achieves the best or the second-best result for each test instance within the test set.

MATERIAL REQUIREMENT PLANNING FOR BUILD-TO-SEQUENCE SUPPLY: A MULTI-LEVEL OPTIMISATION APPROACH

Material requirement planning (MRP) plays an important role in the life of production companies, because it has a great impact on the efficiency of manufacturing operations and it influences the total cost. Just-in-time and just-in-sequence supply offer new solutions to improve the cost-efficiency from inventory holding point of view. Within the frame of this article the author focuses on the build-to-sequence strategy of just-in-sequence supply and demonstrate an integrated approach of material requirement planning. The suggested methodology includes the determination of build-to-sequence orders, the optimisation of material requirement from lead time and cost point of view and determine the optimal sequence of operations to fulfil build-to-sequence demands. The computational results show, that the integrated optimisation leads to an increased cost efficiency, while the required manufacturing operations are performed with a decreased lead time.

Incorporating Compositions in Qualitative Approaches

Qualitative approaches to various tasks, ranging from localization and mapping to active planning, are gaining considerable momentum in recent years. These approaches represent the environment through spatial relationships between small sets of landmarks in independent local coordinate systems. An essential component in these approaches is the composition operator, enabling spatial information propagation between different sets to infer new ones. Integrating compositions within qualitative algorithms brings several difficulties. For instance, if the information required to perform a specific composition operation is unavailable, it must be inferred first, possibly via a preparatory composition operation. This recursive issue becomes more challenging as the amount of information grows. This letter addresses two main questions arising from the above, which remained open: 1. Given an initial set of qualitative spatial relationships, what new ones can be composed? 2. What is the optimal sequence of compositions operations to create a target set among all possible sequences? We provide a theoretical derivation to address the first question and a novel search algorithm to address the second.

Программное средство для нахождения оптимальной последовательности технологических операций производственного процесса лесозаготовительного предприятия

The development of a software tool for finding the optimal sequence of technological operations of the production process of a logging enterprise, as well as the principles of its operation, is considered. Planning of logging production is quite a complex process, since it is necessary to determine the optimal strategy of activity, taking into account a large number of factors, increasing revenue and reducing costs. There are many options for organizing the technological process of harvesting and hauling wood. The program is developed on the basis of the algorithm given in the scientific literature for solving the problem of finding the optimal technological sequence of logging operations in order to automate calculations that are time-consuming. The application is created in the Delphi programming language in the Delhi XE2 RAD Studio development environment. The data used for the calculations is presented in an Excel table. To find the minimum cost flow, the program implements the Basaker — Gowan and Ford — Bellman algorithms. The result of the software tool is the optimal technological chain of delivery of wood from the cutting area to the consumer and corresponding costs. The results are displayed on the screen and can be saved to an Excel file. It is possible for the user to get help information about working with the program. With the help of the developed application, the technological chain of operations for the primary processing and transportation of harvested wood from the cutting area to the consumer is determined, which is characterized by a minimum cost for performing all types of work. The testing showed that the software can be used in practical activities by the head of the logging process at the enterprises of the forest industry.

INTELLIGENT METHOD WITH THE REINFORCEMENT OF THE SYNTHESIS OF OPTIMAL PIPELINE OF THE DATA PRE-PROCESSING OPERATIONS IN THE MACHINE LEARNING PROBLEMS

The paper is devoted to the synthesis and optimization of the pipelines of the data pre-processing operations in the problems of the machine learning models construction. It is noted that it is important to optimize the triad of these pipelines - select optimal sequence of the optimal operations with the optimal parameters. In this case, the change of even one element immediately influences the choice of all other elements and their parameters. In general case, there exists a great number of the admissible variants of such pipelines for each model of machine learning and input data (random values or time series) and, as a rule, there is no marked datasets of model training for the synthesis of such pipelines. The survey of the known approaches to the solution of such problems has been carried out, the conclusion that the best way is to formalize them as the problems of reinforcement  machine learning  has been substantiated t. Typical approaches to the formalization and intellectual methods of similar problems solution have been presented. It is noted that the solution of the problems with reinforcement, as a rule, is complicated due to large dimensionality of the possible sets of the types and subtypes of the operations with different parameters and has problems with the coincidence to really optimal value during limited time. That is why, several improvements, enabling to solve this problem at certain conditions, are suggested. First, it is suggested to allocate variable and constant sections of the pipeline of the data pre-processing operations. It is also suggested for different types of the machine learning models what operations should be referred to the first and last unchangeable links and what operations – to variable link and only to this link it is suggested to apply reinforcement learning. Secondly, the algorithm of the initial setting of RL-policy parameters depending on certain statistical and other characteristics of the input data is suggested. The proposed improvement of the method with the reinforcement of the synthesis of the optimal pipeline of the operations can be applied not only for pre-processing operations but for other problems with the similar data formalization and problem set up.

СОВЕРШЕНСТВОВАНИЕ ТЕХНОЛОГИИ ТЕРМИНАЛЬНОЙ ОБРАБОТКИ КОНТЕЙНЕРНЫХ ПОЕЗДОВ НА ОСНОВЕ ОПТИМИЗАЦИИ ПОРЯДКА ВЫПОЛНЕНИЯ ПЕРЕГРУЗОЧНЫХ ОПЕРАЦИЙ

The authors have analysed automation of operational processes of transport and logistics infrastructure of the JSC «RZD» and have assessed perspectives of development of container terminal automated control systems. The authors propose a mathematical model of execution of loading-unloading operations plan in the form of a problem of linear Boolean programming with complicated restrictions of a combinatorial type for decrease of total duration of container trains processing by handling facilities, decrease of a number of excessive rearrangements of containers and costs of material and technical resources. They have developed a genetic algorithm that gives an optimal sequence of loading-unloading operations. As a result, the paper presents results of numerical experiments that confirm efficiency of the suggested mathematical model and the algorithm.

Buildings Participation in Resilience Enhancement of Community Microgrids: Synergy Between Microgrid and Building Management Systems

Military bases, communities, and university-scale microgrids are being implemented to serve critical loads of high priority. Remote switch scheduling and distributed energy resources (DERs) operation strategies for post-disaster service restoration have been explored in previous literature. Flexible buildings offer the central microgrid management system an opportunity to ensure available energy is directed to the critical loads. This work presents a novel bi-level optimal sequence of operations for managing the controllable devices in small-scale microgrids to serve loads based on a priority scheme in campus-scale microgrids. This study introduces a technique for step-by-step restoration of customers’ granular loads. After and during an outage scenario, the facilities’ internal loads are energized in sequence according to their customer and local criticality levels, as well as the amount of energy available from the bulk system and DERs. The proposed methodology is formulated as a mixed-integer linear programming (MILP) model and adapts to various operating conditions. The proposed method is validated by performing controller hardware-in-loop (CHIL) case studies on the Banshee microgrid benchmark model on a real-time simulator.

Metrological support of maintenance by the technical state of communication means

Modern and prospective communication means are among the most knowledge-driven, high-tech types of industrial products, which are subject to enhanced requirements for quality and efficiency of application. The effective functioning of modern communication means is provided by built-in software, including metrological support. In this paper, we consider the specifics of metrological maintenance of large-scale communication facilities (tens and hundreds of thousands of elements) consisting of separate subunits, the serviceability, maintenance, and recovery of which can be performed autonomously. It is proposed to enhance the efficiency of the by-state-maintenance (also called maintenance by the technical state) by accounting for the specifics of metrological support, establishing an optimal sequence of operations, selecting the measuring instruments in accordance with relevant requirements. To do this, one has to use a combined indicator composed of individual parameters of the tests and the probability of their preferred choice. This allows to assess the technical condition of the communication means with a given probability during a minimal time. In addition, one can estimate the time of maintenance, taking into account the metrological reliability and the probability of correct evaluation of the result of checking the parameters by the measuring instruments. The order of implementation of the obtained results is formalized in the form of an algorithm and an example of their realization is presented.

Pure Hydrolyzable Cellulose from Rice Straw, Wheat Straw and Sugarcane Bagasse by a Simple Scalable Two-Step Treatment

Utilization of amply available lignocellulosic biomass for a cost-effective conversion to renewable chemicals has proven more difficult than anticipated. Sustainable and viable fractionation of any biomass to its individual monomeric components for their further conversion to products at commercial scale therefore remains elusive. A rapid and scalable multi-step pretreatment strategy for fractionation of rice straw using a combination of dilute aqueous acid and aqueous alkali treatment steps under subcritical conditions was investigated. The process steps and parameters were optimized for yield and purity of the resulting biomass components. Effects of acid and alkali concentrations on the fractionation efficiency were studied in the range of 0.2% to 12% w/v at temperatures ranging from 110°C to 200°C for time spanning from 15 to 30 min. The simple optimum sequence of operations and conditions was found to be a diluteacid hydrolysis step at 130°C for 15 min with 2% HNO3 followed by the second treatment step at 130°C for 15 min with 2% NaOH. This combination gave 90% pure cellulose in more than 80% overall yield. Formation of furfurals in the hydrolysate was prevented significantly, and the cellulose obtained showed good amenability for enzymatic hydrolysis to sugars. The same process was applied to wheat straw and sugarcane bagasse, and the obtained results were found to be similar to those obtained for rice straw. The process was successfully scaled up to 50 L batch process with negligible deviations from smaller scale run results.

Methods and algorithms for the synthesis of technologies and programs for control-ling the reconfiguration of on-board systems of small-sized spacecrafts

. In modern conditions, in the field of the creation and use of existing and advanced space vehicles (SV), the issues of autonomy and survivability acquire particular relevance in the development and operation of small-mass spacecraft (SMS) for Earth remote sensing (ERS).
 The specificity of the small spacecraft lies in the fact that it is difficult to directly apply to the process of their creation the standard practice of using the system for ensuring the reliability of the rocket and space industry due to the lack of the ability to provide full structural redundancy of its onboard systems (OBS) associated with mass-dimensional and other restrictions. In this case, the tasks of developing model-algorithmic methods and approaches to ensuring the required level of indicators of structural reliability, survivability and, in general, the effectiveness of the functioning of the MCA OBS become of particular relevance.
 The problem of increasing the level of indicators of autonomy, survivability, efficiency of functioning of complex technical objects (CTO), which, in particular, SMS belong, is considered in the scientific literature in conjunction with solving problems of control, assessment and technical diagnostics of the state of the CTO reconfiguration (structural, functional, structural-functional reconfiguration) of CTO structures, management of its reserves, alternative and multi-mode control, analysis of fault tolerance and disaster recovery of CTO. However, all of these studies are fragmented, both at the methodological and methodological and technological levels.
 The article provides a generalized description of the combined methods and algorithms developed by the authors for solving the problems of synthesis of technologies and programs for controlling the OS reconfiguration to increase the survivability of the SMS. At the same time, these tasks are solved not in isolation, but in a comprehensive manner within the framework of the general problem of proactive management of the structural dynamics of SMS with or without the use of GCC tools, which ensures the efficiency, validity, completeness, isolation and consistency of synthesized management decisions. The novelty of the approach proposed in the article is that its authors, based on the concepts of integrated (system) modeling, proactive control of the structural dynamics of the OS SMS, as well as the intellectualization of the processes of proactive control of the OS SMS, developed methods and algorithms for the synthesis of technologies and programs. Control of the reconfiguration of the MCS BS, providing, firstly, the situational choice of the optimal sequence of operations and the allocation of SMS resources with and without the use of GCC facilities, and, secondly, effective parrying not only of the calculated ones, but also off-design emergency flight situations (EFS), as well as the operational restoration of the operability of its OS. The constructiveness of the proposed approach is illustrated by the example of solving the problem of flexible redistribution of information processing tasks between the OS SMS and the SMS GCC.

A two‐stage service restoration method for electric power distribution systems

Improving the reliability of power distribution systems is critically important for both utilities and customers. This calls for an efficient service restoration module within a distribution management system to support the implementation of self-healing smart grid networks. Although the emerging smart grid technologies, including distributed generators (DGs) and remote-controlled switches, enhance the self-healing capability and allow faster recovery, they still pose additional complexity to the service restoration problem, especially under cold load pickup (CLPU) conditions. Herein, a novel two-stage restoration framework is proposed to generate a restoration solutions with a sequence of control actions. The first stage generates a restoration plan that supports both the traditional service restoration using feeder reconfiguration and the grid-forming DG-assisted intentional islanding methods. The second stage generates an optimal sequence of switching operations to bring the outaged system quickly to the final restored configuration. The problem is formulated as a mixed-integer linear program that incorporates system connectivity, operating constraints, and the CLPU models. It is demonstrated that on using a multi-feeder test case, the proposed framework is effective in utilizing all available resources to quickly restore the service and generate an optimal sequence of switching actions to be used by the operator to reach the desired optimal configuration.

Technological Features in Processing of Conical Lenses

The paper presents the technology of obtaining flat-conical lenses (axicons) by the method of free grinding a work-piece to a flat tool through a layer of abrasive suspension. For this, theoretical and experimental studies of the regularities of stock removal from the base of the cone and its lateral surface have been carried out. The processing modes have been identified that ensure both uniform operation of the flat surface of the part and enhanced removal of the allowance in the edge or central zone of this surface. During the study of the processing of the conical surface, the set-up parameters of the technological equipment have been established, at which there is a minimum deviation of the generatrix of the cone from straightness and maximum productivity of the process. The stages of processing conical lenses are proposed, which allow to assign the optimal sequence of operations in the manufacture of this type of parts from blanks of a cylindrical shape in cases where the ratio of the height of the cone to the diameter of its base H/d £ 0.5. The main stages of processing include: grinding of the bases of cylindrical blanks with maintaining their mutual parallelism with a given accuracy; polishing one of the cylinder bases to achieve the required roughness and deviation from non-flatness; fastening a cylindrical blank to an auxiliary plane-parallel glass plate using molecular cohesion forces; mechanical fastening of a cylindrical work-piece with a collet adapter mandrel for a plane-parallel glass plate; applying the nearest sphere to the second base of the cylindrical blank; drawing a conical surface on the spherical part of a plano-convex lens; grinding and polishing the conical surface to achieve the required roughness and straightness of the cone generatrix. The degree of efficiency of the setup parameters of the machine has been revealed depending on the technological heredity of the work-piece from the point of view of the distribution of the allowance to be removed over the machined surface.

ОБОСНОВАНИЕ ТЕХНОЛОГИЧЕСКИХВОЗМОЖНОСТЕЙСПОСОБАУПРОЧНЯЮЩЕЙ ДЕКОРАТИВНОЙ ОБРАБОТКИ НИЗКОТОВАРНОЙ ДРЕВЕСИНЫ

Hardwood is traditionally used for the production of finishing materials. Its high cost due to scarcity is a key factor holding back demand for solid wood products. Replacing hardwood with low-value and low-liquid raw materials helps to reduce the cost of production. Thanks to the technologies of modifying low-value wood based on pressing and heat treatment, it is possible to improve its natural properties and expand the areas of use. The paper discusses a multistage method of strengthening decorative processing of blanks from low-value wood based on firing, brushing, pressing and heat treatment. The introduction of the method into industry is constrained by the lack of scientifically grounded recommendations for its application. The purpose of the work is to study the possibility of using this method for the manufacture of finishing materials and facing parts. Systematization of information on combined multistage methods of processing wooden blanks has made it possible to identify their significant industrial potential. The differential method of qualimetry substantiates the expediency of a combination of four technological operations: firing, brushing, pressing and heat treatment. Multi-stage processing contributes to a comprehensive increase in aesthetic, physical-mechanical and protective properties of the blanks. The optimal sequence of operations included in the method has been determined through comparative analysis. A prototype of the facing tile was made based on the method. The results of evaluating the operational properties and manufacturing technologies of this facing tile (in comparison with analogues) have confirmed the possibility of using the method of strengthening decorative processing for the manufacture of finishing materials.

Network reconfiguration and DG output including real time optimal switching sequence for system improvement

ABSTRACT This research proposes an optimal switching sequence path method in order to minimise power losses during the network switching operation. Apart from this contribution, simultaneous optimal network reconfiguration and optimal DG output generation were also presented. The proposed methodology involves (1) optimal radial network reconfiguration and DG output simultaneously and (2) real time optimal sequence of switching operations required to convert the network from the original configuration to the optimal configuration obtained from (1). The proposed method is applied to reduce power losses and improve the overall voltage profile of the system. The chosen optimisation techniques include (EP), (PSO), (GSA), and (FA). To assess the capabilities of the proposed method, simulations using MATLAB were carried out on IEEE 33-bus and IEEE 69-bus radial distribution networks. The obtained results demonstrate the effectiveness of the proposed strategy to determine the sequence path of switching operations, as well as the optimal network configuration and optimal generation output of DG units. For more validation, real-time analysis test using PSCAD/EMTDC software is used to check the voltage stability of the system during switching process. The test proved that the optimal switching sequence does not cause over voltage or under voltage when implemented in real-time conditions.