Optimization Of Distribution Networks Research Articles

Phased optimization of active distribution networks incorporating distributed photovoltaic storage system: A multi-objective coati optimization algorithm

In this study, a phased operation optimization method for active distribution network with energy storage system is proposed for the operation optimization problem of active distribution network. The proposed model considers the PV and storage system output, the number of regulation equipment action, network loss and node voltage deviation for multi-objective optimization of the active distribution network. To achieve multi-objective optimization of the proposed optimal model, this study proposes a multi-objective coati optimization algorithm based on integrated crowding distance ranking, Bernoulli chaotic mapping, external archiving and non-dominated ranking mechanism. The proposed algorithm exhibits good performance as tested by ZDT and UF test functions, and obtaining optimal values in 75 % of the results. In the test of the improved IEEE30 and IEEE33 node system, the proposed method is improved by 21.55 %, 56.10 % and 4.12 % in the three aspects of network loss, node voltage deviation and minimum voltage of the whole network compared with the unoptimized case. Therefore, the proposed method in this study has a good application value for the optimization of active distribution network operation and can support the grid-connected operation of distributed new energy sources.

Reactive Power Optimization in Distribution Networks of New Power Systems Based on Multi-Objective Particle Swarm Optimization

The new power system effectively integrates a large number of distributed renewable energy sources, such as solar photovoltaic, wind energy, small hydropower, and biomass energy. This significantly reduces the reliance on fossil fuels and enhances the sustainability and environmental friendliness of energy supply. Compared to distribution networks in traditional power systems, the new-generation distribution network offers notable advantages in improving energy efficiency, reliability, environmental protection, and system flexibility, but it also faces a series of new challenges. These challenges include potential harmonic issues introduced by the widespread use of power electronic devices (such as inverters for renewable energy generation systems and electric vehicle charging stations) and the voltage fluctuations and flickering caused by the intermittency and uncertainty of renewable energy generation, which may affect the normal operation of electrical equipment. To address these challenges, this study proposes an optimization model aimed at minimizing network losses and voltage deviations, utilizing traditional capacitor adjustments and static var compensators (SVCs) as optimization measures. Furthermore, this study introduces an improved version of the multi-objective particle swarm optimization (MOPSO) algorithm, specifically enhanced to address the unique challenges of reactive power optimization in modern distribution networks. The test results show that this algorithm can effectively generate a large number of Pareto optimal solutions. The application of this algorithm on a 33-node network case study demonstrates its advantages in reactive power optimization. The optimization results highlight the effectiveness and feasibility of the proposed improved algorithm in the application of distribution network reactive power optimization, offering users a uniform and diverse range of reactive compensation solutions.

A pareto strategy based on multi-objective optimal integration of distributed generation and compensation devices regarding weather and load fluctuations

In this study, we present a comprehensive optimization framework employing the Multi-Objective Multi-Verse Optimization (MOMVO) algorithm for the optimal integration of Distributed Generations (DGs) and Capacitor Banks (CBs) into electrical distribution networks. Designed with the dual objectives of minimizing energy losses and voltage deviations, this framework significantly enhances the operational efficiency and reliability of the network. Rigorous simulations on the standard IEEE 33-bus and IEEE 69-bus test systems underscore the effectiveness of the MOMVO algorithm, demonstrating up to a 47% reduction in energy losses and up to a 55% improvement in voltage stability. Comparative analysis highlights MOMVO's superiority in terms of convergence speed and solution quality over leading algorithms such as the Multi-Objective Jellyfish Search (MOJS), Multi-Objective Flower Pollination Algorithm (MOFPA), and Multi-Objective Lichtenberg Algorithm (MOLA). The efficacy of the study is particularly evident in the identification of the best compromise solutions using MOMVO. For the IEEE 33 network, the application of MOMVO led to a significant 47.58% reduction in daily energy loss and enhanced voltage profile stability from 0.89 to 0.94 pu. Additionally, it realized a 36.97% decrease in the annual cost of energy losses, highlighting substantial economic benefits. For the larger IEEE 69 network, MOMVO achieved a remarkable 50.15% reduction in energy loss and improved voltage profiles from 0.89 to 0.93 pu, accompanied by a 47.59% reduction in the annual cost of energy losses. These results not only confirm the robustness of the MOMVO algorithm in optimizing technical and economic efficiencies but also underline the potential of advanced optimization techniques in facilitating the sustainable integration of renewable energy resources into existing power infrastructures. This research significantly contributes to the field of electrical distribution network optimization, paving the way for future advancements in renewable energy integration and optimization techniques for enhanced system efficiency, reliability, and sustainability.

An online updated linear power flow model based on regression learning

AbstractThe linear power flow (LPF) model is widely used in the optimization, operation, and control of distribution networks. These applications require the LPF model to be accurate, fast, and simple in order to simplify calculations as well as to efficiently perform operations and scheduling. In addition, it is difficult to realize the online update of parameters in the existing LPF models. The model retraining brings serious data burden and inefficiency. To serve these applications and comply with requirements, a brand new LPF model is proposed in this paper. A quadratic power flow model is trained by regression learning first, and then the proposed LPF model is derived by Taylor expansion. After only one initial regression learning, the proposed LPF model no longer needs retraining when updated. The refreshed parameter is simply updated online according to the real‐time measurement data, which improves the generalization ability. In conclusion, the proposed LPF model is accurate, generalizable, and greatly minimizes the data consumption and running time. Performance analysis verifies these superiorities.

Optimal quantum key distribution networks: capacitance versus security

The rate and security of quantum communications between users placed at arbitrary points of a quantum communication network depend on the structure of the network, on its extension and on the nature of the communication channels. In this work we propose a strategy for the optimization of trusted-relays based networks that intertwines classical network approaches and quantum information theory. Specifically, by suitably defining a quantum communication efficiency functional, we identify the optimal quantum communication connections through the network by balancing security and the quantum communication rate. The optimized network is then constructed as the network of the maximal quantum communication efficiency connections and its performance is evaluated by studying the scaling of average properties as functions of the number of nodes and of the network spatial extension.

Enhancing Distribution Network Efficiency with Andean Condor Algorithm-Driven Optimal Placement of Distributed Generation and Network Reconfiguration

This research introduces an innovative methodology for optimizing placement of Distributed Generations (DGs) within distribution networks, with primary focus on reducing power losses and enhancing energy efficiency. The surge in electric power demand has necessitated integration of DGs into distribution networks, bolstering system security. However, this integration brings about structural changes and alters system parameters, such as power fluctuations, voltage profiles and fault current levels. To address these complexities, this research contributes comprehensive approach that integrates Andean Condor Algorithm (ACA) and Network Reconfiguration (NR) to optimize DG placement. The proposed methodology aims to minimize both active and reactive power losses in distribution networks. MATLAB simulations using the 69 and 33 bus distribution networks are used to thoroughly validate the system’s efficacy. The suggested ACA achieves an active power loss of 120 KW and a reactive power loss of 28.66 KVAr for 33 bus system. In the same way, 69 bus system manages to achieve a 16 KVAr reactive power loss and a 92.3 KW active power loss. The results show that suggested methodology is both effective and better than popular CPSO method, which makes it valuable addition to the field of distributed generation optimization in power distribution networks.

Reactive power optimization of distribution network with photovoltaic power based on sparrow search algorithm

With the construction of new power systems centered around new energy, distributed photovoltaic power generation has been rapidly popularized. Photovoltaic power generation with volatility and randomness is connected to the distribution network, causing voltage fluctuations in the distribution network to exceed allowable values and an increase in system losses. The objective of this article is to establish a mathematical model for optimizing the distribution of reactive power using distributed photovoltaic power generation, to minimize system losses and voltage fluctuations. Using an improved sparrow search algorithm to solve the model and taking the IEEE30 node system as an example for simulation analysis, the feasibility of the model and algorithm is verified through case analysis.

Hybrid genetic algorithm-simulated annealing based electric vehicle charging station placement for optimizing distribution network resilience

Rapid placement of electric vehicle charging stations (EVCSs) is essential for the transportation industry in response to the growing electric vehicle (EV) fleet. The widespread usage of EVs is an essential strategy for reducing greenhouse gas emissions from traditional vehicles. The focus of this study is the challenge of smoothly integrating Plug-in EV Charging Stations (PEVCS) into distribution networks, especially when distributed photovoltaic (PV) systems are involved. A hybrid Genetic Algorithm and Simulated Annealing method (GA-SAA) are used in the research to strategically find the optimal locations for PEVCS in order to overcome this integration difficulty. This paper investigates PV system situations, presenting the problem as a multicriteria task with two primary objectives: reducing power losses and maintaining acceptable voltage levels. By optimizing the placement of EVCS and balancing their integration with distributed generation, this approach enhances the sustainability and reliability of distribution networks.

Multi-objective optimization scheduling method considering flexible load integration for distribution network

In response to a series of issues in the distribution network, such as inadequate and inflexible utilization of flexible loads, delayed response to demand participation, and the uncertainty of new energy source output, a differentiated objective-based method for optimizing distribution network operations is proposed. Firstly, flexible loads are categorized, and corresponding mathematical models are established. Secondly, by employing chance-constrained programming to account for the uncertainty in new energy source output, a multi-objective optimization model is developed to reduce distribution network economic costs, decrease network losses, and enhance power supply reliability. Subsequently, an improved NSGA-III algorithm is introduced to address the multi-objective model. Finally, an 11-node distribution network is used as a case study, and three different algorithms are comprehensively compared. This confirms the rationality of the optimized scheduling scheme proposed in this paper.

Sustainable urban logistics: A case study of waterway integration in Amsterdam

This paper tackles the growing challenges in urban logistics by presenting an optimal distribution network that integrates urban waterways and last-mile delivery, tailored for cities boasting extensive waterway networks. We examine Amsterdam's city center as a case study, prompted by the strain on quay walls, congestion, and emissions, urging a reevaluation of its urban logistics design. We formulate the problem as a two-echelon location routing problem with time windows and introduce a hybrid solution approach for effective resolution. Our algorithm consistently outperforms existing methods, with a superior solution quality, demonstrating its effectiveness across established and newly developed benchmark instances. In our case study, we evaluate the benefits of transitioning from a roadway-centric to a waterway-based system, showcasing significant cost savings (approximately 28 %), reductions in vehicle weight (approximately 43 %), and minimized travel distances (approximately 80 %) within the city center. The integration of electric vehicles enhances environmental sustainability, resulting in a total daily emission reduction of 43.46 kg. Our study underscores the untapped potential of inland waterways in easing urban logistics challenges. Inspired by Amsterdam's experience, global cities can adopt innovative approaches for sustainable logistics, providing valuable insights for managers striving to enhance efficiency, cut costs, and promote sustainable transportation practices.

Cascading failure and resilience optimization of unmanned vehicle distribution networks in IoT

With the development of science and technology, unmanned vehicles have become one of the main ways of distribution, and the appearance of unmanned vehicles undoubtedly brings great convenience to people. However, few researchers consider the cascading failure times for different situations in unmanned vehicle distribution networks. Especially, few researchers have integrated them into maintenance strategies to real-time path changes to increase the reliability of accomplishing distribution tasks and improve resilience after congestion. One of the key issues is how to improve the resilience of distribution network and what maintenance strategies should be developed. In this paper, cascading failures are analyzed and resilience is optimized in unmanned vehicle distribution network. First, cascading failures for different situations are analyzed, and two importance-based diversion methods are proposed to describe the three processes of congestion formation, persistence, and evacuation. Second, failure and performance degradation processes are investigated. Third, the Floyd time-varying resilience optimization algorithm is proposed, and the calculation method of resilience under different strategies is given. At last, simulation results show that the proposed strategy 3 improves the original resilience by 101 % compared with the traditional waiting path strategy.

Multi-criteria decision-making methods for optimal design of intermittent water distribution network

ABSTRACT Multi-objective optimization of water distribution networks (WDNs) is crucial for achieving a balance between cost, equity, and reliability. The present study conducts a comparative analysis, evaluating the effectiveness of four multi-objective optimization algorithms implemented within the EPANET framework for the optimal design of three distinct WDNs taken from the literature. The networks were optimized considering three objectives: maximizing network resilience, and uniformity coefficient (CU, a measure of uniformity), alongside minimizing the cost of the network. Further, multi-criteria decision-making (MCDM) analysis is carried out, encompassing various MCDM techniques and weighting methods, to explore the sensitivity of solution rankings. In Network 1, non-dominated sorting genetic algorithm 2 (NSGA-II) produced suboptimal solutions with CU and In values below 0.4, while caRamel, multi-objective particle swarm optimization based on crowding distance (MOPSO-CD), and multi-objective evolutionary algorithm with decomposition (MOEA/D) showcased increasing CU and In values towards 1, paralleled by a rise in network cost. The performance of NSGA-II in Network 3 was unsatisfactory (CU < 0.32), while the other algorithms demonstrated satisfactory values (>0.98) for reliability and (>0.5) for CU. The findings offer valuable insights into cost-effective strategies for achieving equity in water supply without substantial impact on overall network cost. The findings of the study also underscore the sensitivity of ranking outcomes in MCDM analysis to the choice of technique and weighting method.

Voltage Optimization in Active Distribution Networks—Utilizing Analytical and Computational Approaches in High Renewable Energy Penetration Environments

This review paper synthesizes the recent advancements in voltage regulation techniques for active distribution networks (ADNs), particularly in contexts with high renewable energy source (RES) penetration, using photovoltaics (PVs) as a highlighted example. It covers a comprehensive analysis of various innovative strategies and optimization algorithms aimed at mitigating voltage fluctuations, optimizing network performance, and integrating smart technologies like smart inverters and energy storage systems (ESSs). The review highlights key developments in decentralized control algorithms, multi-objective optimization techniques, and the integration of advanced technologies such as soft open points (SOPs) to enhance grid stability and efficiency. The paper categorizes these strategies into two main types: analytical methods and computational methods. In conclusion, this review underscores the critical need for advanced analytical and computational methods in the voltage regulation of ADNs with high renewable energy penetration levels, highlighting the potential for significant improvements in grid stability and efficiency.

Çok Seviyeli Rekabetçi Kapalı Döngü Tedarik Zinciri Ağ Tasarımı: Karşılaştırmalı Bir Analiz

Kısıtlı doğal kaynaklar ve bunların bilinçsizce tüketilmesi, son yıllarda hammadde yerine atık malzemenin işlenme oranının artmasına yol açmış ve endüstriyel ortamı daha rekabetçi hale getirerek, Tedarik Zinciri Yönetiminin (TZY) dinamiklerini değiştirmiştir. Kullanım ömrü sonu ürün sayısının artması ve bunlarla ilgili çevresel kaygılar, aynı zamanda müşteri baskılarına cevap verebilmek adına lojistiğin tersine çevrilmesine büyük özen gösterilerek Kapalı Döngü Tedarik Zinciri (KDTZ) tasarımı ve optimizasyonu üzerine ilgi artmıştır. KDTZ, geniş bir literatüre sahiptir ve bu çerçevede tedarik zinciri maliyetlerini optimize etmek için pek çok model geliştirilmiştir. Literatürün çoğu tekil tedarik zinciri ile ilgilidir ve mevcut rakip tedarik zincirlerini görmezden gelir. Ancak, günümüzün rekabetçi pazarlarında, tedarik zincirleri birkaç rekabetçi şirket tarafından birbirine entegre şeklinde oluşturulmakta ve daha fazla pazar payı elde etmek için birlikte rekabet ederek çalışmaktadır. Böyle bir ortamda, tedarik zincirleri içinde ve arasında farklı rekabet biçimleri vardır. Bu bağlamda, makale ürün geri kazanımı için bir takım teşviklerle kullanılmış ürünleri iade etme isteğini artırarak sürdürülebilir tüketimi iyileştirmeye çalışan iki üretici, bir toplama&geri dönüşüm merkezi ve müşterilerden oluşan çok seviyeli rekabetçi KDTZ yönetiminde dağıtım ağlarının tasarımı ve optimizasyonu problemini ele almaktadır. KDTZ yönetiminde farklı amaçlara sahip karar vericiler için uzlaşık çözüme ulaşmak için dört farklı Etkileşimli Bulanık Programlama (EBP) yaklaşımı kullanılmış ve sonuçlar analiz edilmiştir.

A novel framework for implementing optimal power distribution network planning approaches

A novel framework for implementing optimal power distribution network planning approaches

Correction to: Design Optimization of Water Distribution Networks with Dynamic Search Space Reduction GA

Correction to: Design Optimization of Water Distribution Networks with Dynamic Search Space Reduction GA

Multi-objective reactive power optimization for low voltage distribution networks based on improved marine predator algorithm

A large number of distributed photovoltaic generators are constructed in the 0.4 kV distribution network, which causes the distribution network’s power flow and voltage amplitude to change, and brings new challenges to the reactive voltage control. Firstly, a reactive power adjustment model is invented, and the objective of the optimization is to minimize the distribution network line losses and node voltage shifts. Secondly, the standard marine predator algorithm is improved by using Bernoulli mapping, nonlinear attenuation weight function, and elite replacement strategy, and the model is solved by the improved algorithm. The results of the example optimization of a 21-node low-voltage distribution network show that the presented reactive power adjustment method can control the voltage point within a reasonable range and reduce the line loss rate. The improved marine predator algorithm has better global optimality finding and faster convergence than traditional intelligent algorithms.

Multiple-Zone Synchronous Voltage Regulation and Loss Reduction Optimization of Distribution Networks Based on a Dual Rotary Phase-Shifting Transformer

For the problem in which accessing a high proportion of renewable energy results in exceeding the limit in distribution network voltage, the existing regulating method experiences difficulty in considering the two-way voltage regulation and loss reduction optimization function. This study proposes a series-type dual rotary phase-shifting transformer (DRPST) based on the principle of phase volume synthesis. This transformer exhibits bidirectional voltage regulation, high reliability, and low cost. First, the topology, operating principle, and equivalent circuit of DRPST are introduced, and its simplified circuit model is established. On the basis of this model, the causes of voltage exceeding the limits are analyzed and the active distribution network model that contains DRPST is constructed. A real-time rolling two-layer optimization strategy based on DRPST is proposed. The inner layer model is solved using the multi-objective particle swarm optimization algorithm with the objective of minimizing voltage deviation and line loss. The optimal compromise solution of the Pareto solution set of the inner layer model is determined using the fuzzy subordinate degree function method. The outer model is based on the optimal compromise solution of the inner model, and the DRPST output rotor angle is controlled without deviation through double closed-loop proportional–integral regulation. Finally, the correctness and effectiveness of the proposed topology and control method are verified via simulation and experimental analysis.

Optimization of Rice Distribution Network Based on Green Logistics Concept

SDGs or Sustainable Development Goals have become a global and national commitment. Greenhouse gases (GHG) are indicators of climate change which is one of the 17 SDG’s goals. In the national food logistics system with rice as a staple food, there are some regions with surplus production and other deficits. Hence, the distribution involved the transportation activities that need to be carried out. In transportation, the carbon footprints are generated from exhaust gases as residue from the fuel combustion process. The carbon footprint efficiency is one of the indicators in green logistics. Green logistics for decarbonization in the transportation sector for a better environment has to start massively. Indonesia is committed to implementing the green logistics concept in logistics distribution. In this case, the author determined the surplus area and the deficit one based on statistical data on the production and consumption of each city in Java. Furthermore, a distribution network model is designed that involves emission level as a parameter in determining the route on the network. In the design, the selected mode of freight transportation was limited to land transportation modes besides trains. The design makes environmental aspects a major consideration in the distribution as well as the shipping costs.
 Keywords: carbon footprint, emission factor, green logistics

Reactive power convex optimization of active distribution network based on Improved Grey Wolf Optimizer

Reactive power convex optimization of active distribution network based on Improved Grey Wolf Optimizer