Optimization Of Distribution Networks Research Articles

Optimization of Pressurized Tree-Type Water Distribution Network Using the Improved Decomposition–Dynamic Programming Aggregation Algorithm

Pressurized tree-type water distribution network (WDN) is widely used in rural water supply projects. Optimization of this network has direct practical significance to reduce the capital cost. This paper developed a discrete nonlinear model to obtain the minimum equivalent annual cost (EAC) of pressurized tree-type WDN. The pump head and pipe diameter were taken into account as the double decision variables, while the pipe head loss and flow velocity were the constraint conditions. The model was solved by using the improved decomposition–dynamic programming aggregation (DDPA) algorithm and applied to a real case. The optimization results showed that the annual investment, depreciation and maintenance cost (W1) were reduced by 22.5%; however, the pumps’ operational cost (p) increased by 17.9% compared to the actual layout. Overall, the optimal EAC was reduced by 15.2% with the optimized pump head and optimal diameter distribution of the network. This method demonstrated an intrinsic trade-off between investment and operational cost, and provided an efficient decision support tool for least-cost design of pressurized tree-type WDN.

Optimization of Distribution Network Construction Based on GIS System

With the rapid development of China’s economy and science and technology, people’s daily life and scientific research work demand more and more electricity. The traditional distribution network structure and load can hardly meet the actual demand. Therefore, it is very necessary to construct a more stable distribution network system in accordance with the actual demand for electricity. This paper mainly introduces the application of GIS system in power distribution network planning and design, and realizes the optimization and upgrading of power distribution network in coordination with the geographic information of the construction site, so as to meet the increasing demand for electricity.

Multi-objective Comprehensive Optimization of Distribution Network considering the randomness of DG

In recent years, distributed generation technology develops rapidly due to its’ flexible and environment-friendly nature. In order to better analyze the impact of DG on the economics and safety of distribution networks, a probability model for random load, micro gas turbines and photovoltaic power generation system is formulated. With the objective of minimizing the network loss, lowest static insecurity probability, and the lowest cost of purchasing electricity, the optimization of distribution network with distributed generation is carried out by adjusting the distribution network topology and the output of controllable DG. The stochastic power flow is combined with the particle swarm optimization algorithm to obtain the Pareto non-inferior solution set, and then the subject is selected to obtain the optimal solution. Finally, simulations are carried out on the IEEE 33-bus test system and it is shown that the optimized network can effectively reduce the network loss and static insecurity probability on the basis of low cost of purchasing electricity.

Effective network reconfiguration with distributed generation allocation in radial distribution networks using water cycle algorithm

This paper presents a novel application of watercycle algorithm (WCA) for optimal distribution network reconfiguration (DNR) and distributed generation (DG) allocation in radial distribution network (RDN). WCA is proposed to simultaneously have the optimal topology of RDN and get the optimal DG size and placement. The objective is to minimize power losses and the voltage profile index. in addition, study the variation in both voltage magnitude and voltage stability profiles. Loss sensitivity factor index is utilized to crop the candidate buses for DG allocation aiming to narrow the search space for DG sizing. Moreover, the graph theory is presented to get the fundamental loops that used to reduce the search space for DNR by limiting the infeasible configurations during the optimization process, where the connection matrix of the radial network is used to check the radial constraint of each configuration. The proposed method has been applied on two different RDN with different scenarios. The obtained results verify the effectiveness of the proposed algorithm in comparison with previous works in literature.

Investigation of economic and environmental impacts of optimization and correction of sugar beet distribution network in Iran’s sugar supply chain

سابقه و هدف: با توجه به هزینه­ های بالا حمل و نقل در بخش شکر تولیدی از چغندرقند ﻣﺪﯾﺮﯾﺖ منطقی زﻧﺠﯿﺮه­ یﺗﺎﻣﯿﻦ آن از اﻫﻤﯿﺖ ﺧﺎﺻﯽ ﺑﺮﺧﻮردار اﺳﺖ ﮐﻪ ﻣﯽ­ﺗﻮاﻧﺪ در ﮐﺎﻫﺶ ﻫﺰﯾﻨﻪ، ﮐﺎﻫﺶ آﻻﯾﻨﺪه­ﻫﺎی ﻫﻮا، ﻣﮑﺎن­ﯾﺎﺑﯽ ﺗﺴﻬﯿﻼت، ﺗﺤﻮﯾﻞ ﺑﻪ ﻣﻮﻗﻊ خدمات ﺑﻪ ﻧﻘﺎط ﺗﻘﺎﺿﺎ و رﺿﺎﯾﺖ ﻣﺸﺘﺮی را ﺑﻪ ﻫﻤﺮاه داﺷﺘﻪ ﺑﺎﺷﻨﺪ. بررسی مطالعه­ های گذشته نشان می ­دهد که بررسی­ های اندکی در زمینه بهینه ­یابی شبکه توزیع محصول ­های کشاورزی در زنجیره تامین با در نظر گرفتن تاثیر اقتصادی و محیط زیستی انجام شده است. بنابراین تحقیق حاضر به بهینه ‏سازی شبکه توزیع چغندر قند از منطقه­ های تولیدی به کارخانه­ های بهره­ برداری و تصفیه چغندرقند در زنجیره تامین شکر ایران و تعیین تاثیر اقتصادی و محیط زیستی محیطی آن می ‏پردازد. در این پژوهش افزون بر مقایسه هزینه ‏های حمل و نقل در وضعیت توزیع موجود و بهینه، تغییرات در مصرف سوخت به ازای تغییر در توزیع چغندرقند، کاهش انتشار ناشی از اصلاح شبکه حمل و نقل و منافع اقتصادی آن در کشور نیز محاسبه شد. مواد و روش ­ها: مطالعه حاضر با استفاده از آمار و اطلاعات مرکز آمار ایران در سال 1393 برای10 استان تولیدکننده چغندرقند شامل آذربایجان غربی، کرمانشاه، خوزستان، فارس، یاسوج، لرستان، سمنان، قزوین، خراسان رضوی و خراسان شمالی و 24 مرکز تقاضا انجام شد. این مطالعه با استفاده از الگوی برنامه ­ریزی ریاضی و مدل‏سازی حمل و نقل به تجزیه و تحلیل داده­ها با بکارگیری نرم افزار GAMS پرداخت. نتایج و بحث: نتایج اجرای برنامه بهینه حمل و نقل نشان داد اجرای برنامه پیشنهادی سبب صرفه ­جویی در هزینه ­ها به میزان 43.7 میلیارد ریال معادل 3.84 درصد شد. همچنین، نتایج اجرای برنامه بهینه حمل و نقل با در نظر گرفتن اثرهای محیط زیستی، نشان­دهنده­ی کاهش مصرف سوخت به میزان 2 میلیون و 177 هزار لیتر گازوئیل و منافع محیط زیستی حدود 5.7 میلیارد ریال است. نتیجه ­گیری: بنابراین می ­توان نتیجه گرفت اجرای برنامه بهینه حمل و نقل سبب کاهش هزینه­های حمل و نقل و کاهش مصرف سوخت شده است. لذا اجرای برنامه بهینه افزون­ بر کاهش هزینه ­ی حمل و نقل و قیمت تمام شده شکر سبب کاهش آلودگی و مصرف سوخت در کشور نیز می‏ شود. بر اساس نتایج به ­دست آمده، پیشنهاد می­ شود، در جریان برنامه ­ریزی حمل و نقل در زنجیره تأمین شکر، از الگوی پیشنهادی در این مطالعه برای حمل و نقل چغندرقند استفاده شود، زیرا اجرای برنامه بهینه بر کاهش هزینه ­ی حمل و نقل و کاهش مصرف سوخت فسیلی و در نتیجه بر قیمت تمام شده شکر تاثیر می‏ گذارد.

Multi-Objective Reactive Power Optimization Including Distributed Generation

In order to solve the problem of reactive power optimization of distribution network with distributed power supply, the multi-objective reactive power optimization function is established from multiple perspectives, and the equation constraint and inequality constraint equation of power system are considered. Secondly, taking IEEE33 node distribution system with distributed power supply as an example, reactive power optimization of single objective function is carried out to verify that the proposed algorithm has a global convergence and a great advantage in convergence speed. Finally, multi-objective reactive power optimization of distribution network with distributed power supply is carried out. Simulation results demonstrate the effectiveness of the proposed algorithm.

An integrated model for optimizing distribution network with considering assembly line balancing

An integrated model for optimizing distribution network with considering assembly line balancing

New approach to solving the clustered shortest-path tree problem based on reducing the search space of evolutionary algorithm

Along with the development of manufacture and services, the problem of distribution network optimization has been growing in importance, thus receiving much attention from the research community. One of the most recently introduced network optimization problems is the Clustered Shortest-Path Tree Problem (CluSTP). Since the problem is NP-Hard, recent approaches often prefer to use approximation algorithms to solve it, several of which used Evolutionary Algorithms (EAs) and have been proven to be effective. However, most of the prior studies directly applied EAs to the whole CluSTP problem, which leads to a great amount of resource consumption, especially when the problem size is large. To overcome these limitations, this paper suggests a method for reducing the search space of the EAs applied to CluSTP by decomposing the original problem into two sub-problems, the solution to one of which is found by an EAs and that to the other is found by another method. The goal of the first sub-problem is to determine a spanning tree which connects among the clusters, while the goal of the second sub-problem is to determine the best spanning tree for each cluster. In addition, this paper proposes a new EAs, which can be applied to solve the first sub-problem and suggests using the Dijkstra’s algorithm to solve the second sub-problem. The proposed approach is comprehensively experimented and compared with existing methods. Experimental results prove that our method is more efficient and more importantly, it can obtain results which are close to the optimal results.

Issue: Selected paper of OIPE 2016 on modeling and optimal design of electromagnetic and electronic devices

Issue: Selected paper of OIPE 2016 on modeling and optimal design of electromagnetic and electronic devices

Decentralized Load Estimation for Distribution Systems Using Artificial Neural Networks

In this paper we present a decentralized state estimation method to support real-time Volt/Var optimization in distribution network with high penetration of distributed generation. The network is divided into sub-areas according to the location of the generation units and the mutual information (MI) between the states of interest and the available measurements. The proposed decentralized state estimation scheme only relies on local information and on a limited amount of information from neighboring areas. In each area, an artificial neural network (ANN) is used to estimate the loads consumptions. The proposed approach is tested using a modified IEEE 34-node test feeder. The effectiveness of the method is validated on a Hardware-In-the-Loop (HIL) simulation platform. To evaluate the accuracy and efficiency of the proposed decentralized approach we compared the results obtained to a centralized and a totally local approach.

Reactive power optimization of an active distribution network including a solid state transformer using a moth swarm algorithm

This paper presents a reactive power optimization model for an active distribution network (ADN), including a solid state transformer (SST). In this model, the SST, as a power electronic device, compensates the system reactive power by adjusting the modulation coefficients and phase angles of the voltage source converter. A sequential multiscenario technique is implemented to simulate the transient characteristics of distributed generators (DGs). By optimizing the continuous variables of the SST and the reactive power output of a controllable DG, voltage deviation and power loss of the ADN are minimized. Meanwhile, although the cost of an SST is higher than that of a traditional transformer, using this model reduces the total cost of the system through reactive power optimization. In addition, a moth swarm algorithm is proposed. By adopting a position updating strategy of Lévy mutation, lateral positioning, and celestial navigation, the convergence speed and global optimization capability of the algorithm are improved. Finally, the rationality and validity of the proposed model and algorithm are verified by simulation of the IEEE 33-bus system.

Testing evolutionary algorithms for optimization of water distribution networks

Water distribution networks (WDNs) are one of the most important elements of urban infrastructure and require large investment for construction. Design of WDNs is classified as a large combinatorial discrete nonlinear optimization problem. The main concerns associated with the optimization of such networks are the nonlinearity of the discharge-head loss relationships for pipes and the discrete nature of pipe sizes. Due to these issues, this problem is widely considered to be a benchmark problem for testing and evaluating the performance of nonlinear and heuristic optimization algorithms. This paper compares different techniques, all based on evolutionary algorithms (EAs), which yield optimal solutions for least-cost design of WDNs. All of these algorithms search for the global optimum starting from populations of solutions, rather than from a single solution, as in Newton-based search methods. They use different operators to improve the performance of many solutions over repeated iterations. Ten EAs, four of them for the first time, are applied to the design of three networks and their performance in terms of the least cost, under different stopping criteria, are evaluated. Statistical information for 20 executions of the ten algorithms is summarized, and Friedman tests are conducted. Results show that, for the two-loop benchmark network, the particle swarm optimization gravitational search and biology and bioinformatics global optimization algorithms efficiently converge to the global optimum, but perform poorly for large networks. In contrast, given a sufficient number of function evaluations, the covariance matrix adaptation evolution strategy and soccer league competition algorithm consistently converge to the global optimum, for large networks.

Omni-Channel Product Distribution Network Design by Using the Improved Particle Swarm Optimization Algorithm

Distribution network under Omni-Channel integration contains many levels. There are one or more dealers at each level which forms a many-to-many distribution network. Consumers purchase a wide variety of products and their demands are uncertain, which constitutes a complex demand network and increases the complexity of the supply chain network. This paper focuses on the integrated optimization of supply chain distribution network and demand network and constructs the joint randomization planning model of location and routing. The goal is to minimize the total costs of the supply chain network under uncertain customer demands. Based on the traditional particle swarm optimization (PSO), this study introduces the collaborative idea to reduce the coding dimension, improves the boundary processing strategy, and adopts the mutation operator to expand the search space. A case study of distribution under Omni-Channel integration in a large enterprise was done. The validity of the model and the effectiveness of the proposed method were verified by numerical experiments.

Design of Water Distribution Network by Heuristic Approach

Optimization of Water Distribution Network (WDN) is a NP hard type and extremely complex problem due to non-linear relationship between Flow and Head loss and discrete pipe diameters. Benchmark problem Hanoi Network is solved herewith by Heuristic methodology using Velocity as Implicit Function by using Candidate diameters obtained from Critical Path Concept. The simulation is performed using EPANET Software. The results obtained are compared with other meta-heuristic methods like Genetic Algorithm, Simulated Annealing and GA-ILP method. The present study concludes that the results are close to optimal values reported in literature in minimum number of iterations.

Energy Management Strategy in Dynamic Distribution Network Reconfiguration Considering Renewable Energy Resources and Storage

Penetration of renewable energy sources (RESs) and electrical energy storage (EES) systems in distribution systems is increasing, and it is crucial to investigate their impact on systems’ operation scheme, reliability, and security. In this paper, expected energy not supplied (EENS) and voltage stability index (VSI) of distribution networks are investigated in dynamic balanced and unbalanced distribution network reconfiguration, including RESs and EES systems. Furthermore, due to the high investment cost of the EES systems, the number of charge and discharge is limited, and the state-of-health constraint is included in the underlying problem to prolong the lifetime of these facilities. The optimal charging/discharging scheme for EES systems and optimal distribution network topology are presented in order to optimize the operational costs, and reliability and security indices simultaneously. The proposed strategy is applied to a large-scale 119-bus distribution test network in order to show the economic justification of the proposed approach.

Convex Modeling of Pumps in Order to Optimize Their Energy Use

AbstractThis study presents convex modeling of drainage pumps so that real‐time control systems can be implemented to minimize their energy use. A convex model is built based on pump curves and then used in mixed‐integer optimization to allow pumps to be turned on or off. It is implemented as an extension to the open source software package RTC‐Tools. The formulation is such that the continuous relaxations of the mixed‐integer problem are convex, hence branch‐and‐bound techniques may be used to find a global optimum. The formulation can be used for variable‐speed and constant‐speed pumps. There are several possible applications, such as optimization of polder systems, pumped‐storage systems, or certain water distribution networks. Finally, an example of the drainage pump is presented to compare the method to current methods and show that energy can be saved by using the proposed method.

Multi-objective operation optimization of active distribution network based on three-terminal flexible multi-state switch

To operate the distribution network properly is a great challenge when numerous intermittent distributed generators are involved. A novel approach for solving this issue is proposed in this paper, which is based on the three-terminal flexible multi-state switch. The three-terminal flexible multi-state switches, which are controllable, are adopted to replace the traditional tie switch and to increase the flexibility of distribution network operation and control. The timing characteristics of distribution network operation are considered, and the controllable load and the three-port flexible multi-state switch are introduced into this methodology. A multi-objective operation optimization model for an active distribution network is developed, i.e., the objective functions are designed to minimize the operating cost of the distribution network and to minimize the voltage deviation in multiple periods with consideration of demand response according to the characteristics of the active distribution network. The effectiveness of the proposed method is validated based on the IEEE 33-node test feeder by the combined Particle Swarm Optimization algorithm. The results show that the coordinated optimization of the flexible multi-state switch and the demand response improve the distribution network voltage levels significantly, which will not only bring the economic efficiency but also improve the safety and reliability of the system.

A graph theory application for fast and efficient search of optimal radialized distribution network topology

The topological changes and re-configuration can be brought into an existing power network by operation of sectionalizing switches on the feeder lines. Distribution networks can be operated either in meshed or in radial configuration. While primary meshed distribution networks have advantages like lower short circuit current, reduced overall conductor length and increased reliability and fault tolerance, the radial networks on the other hand stand-out on their merits of simple installation and easier relay coordination. An optimal radial network can operate at higher efficiency owing to its reduced power losses and can offer higher voltage stability. Transformation of a primary meshed network to a radially reconfigured optimal network requires selection of the “best” set of switches on the feeders that are to be opened or closed so that each feeder can be operated close to its maximum loadability limit and the resulting optimal network operates at its lowest feasible value of power loss and highest voltage stability index. This theoretical concept of optimal power network radialization is however difficult to implement in practice due to the heavy computational burden and unacceptably long searching time as the number of feasible alternative configurations is usually massive for large power networks. Given the extent of the search space, the heuristic search approaches are usually more practical and common in power network reconfiguration. This paper presents a novel guided search approach based on Edmond’s Maximal Spanning Tree Algorithm to achieve the optimal radial configuration for an arbitrary power network rapidly and efficiently. Efficacy of the proposed method has been tested on 30-node and 57-node mesh distribution networks with encouraging results.

Multi-objective Optimization of Distribution Network Based on Model Predictive Control

With the increasing penetration of distributed generation(DG) in distribution network and the rising demand of electricity users, the uncertainty of distributed generation output and randomness of load will lead to over-voltage and power backward in distribution network. This paper proposes a multiobjective active and reactive power coordination optimal control strategy, which based on multi-time scale model predictive control (MPC). The objectives of proposed strategy consists of increasing distributed energy consumption, decreasing grid voltage deviation and decreasing system network loss, which are implemented in long time scale. And in short time scale, the energy storage active power is adjusted according to the prediction errors of load and new energy power generation. The effectiveness of the proposed optimization scheme is verified in simulation analysis.

A Distributionally Robust Reactive Power Optimization Model for Active Distribution Network Considering Reactive Power Support of DG and Switch Reconfiguration

Abstract In recent years, the rapid development of the Distributed Generation (DG) has brought new challenges to the reactive power optimization in the active distribution network. The traditional stochastic optimization and robust optimization methods may result in over-conservative or over-risky decisions when dealing with the uncertainty of renewable energies. Therefore, this paper proposes a distributionally robust reactive power optimization model for active distribution network, which regards the network loss as the its optimization target, and considers the reactive power support of and the switch reconfiguration. This model includes not only switching, on load tap changer (OLTC), discrete and continuous reactive power compensation devices usually contained in traditional reactive power optimization, but also the detailed model of capacity curve of Double-Fed Induction Generator and Micro-Gas Turbine. After the reactive power support is integrated, the linearization method of active and reactive power output coupling characteristics constraint is specially designed. Then, based on the typical scenario data of wind and photovoltaic power, and the regulation characteristics for different decision variables, a data-driven based two-stage distributionally robust reactive power optimization model is set up, in which the uncertainty probability distribution confidence set is simultaneously constrained by 1-norm and ∞-norm. Finally, by the Columns and Constraints Generation algorithm, the IEEE 33-bus system is applied to verify the effectiveness of proposed.