Review on distribution network optimization under uncertainty

With the increase of renewable energy in electricity generation and increased engagement from demand sides, distribution network planning and operation face great challenges in the provision of stable, secure and dedicated service under a high level of uncertainty in network behaviors. Distribution network planning and operation, at the same time, also benefit from the changes of current and future distribution networks in terms of the availability of increased resources, diversity, smartness, controllability and flexibility of the distribution networks. This paper reviews the critical optimization problems faced by distribution planning and operation, including how to cope with these changes, how to integrate an optimization process in a problem-solving framework to efficiently search for optimal strategy and how to optimize sources and flexibilities properly in order to achieve cost-effective operation and provide quality of services as required, among other factors. This paper also discusses the approaches to reduce the heavy computation load when solving large-scale network optimization problems, for instance by integrating the prior knowledge of network configuration in optimization search space. A number of optimization techniques have been reviewed and discussed in the paper. This paper also discusses the changes, challenges and opportunities in future distribution networks, analyzes the possible problems that will be faced by future network planning and operations and discusses the potential strategies to solve these optimization problems

Distribution Network Optimization Based on Genetic Algorithm

This paper presents a new robust optimization technique for distribution network configuration, which can be regarded as a modification of the recently developed genetic algorithm. The multi-objective genetic algorithm has been applied to this problem to optimize the total cost while simultaneously minimize the power loss and maximize the voltage profile. The IEEE 69-bus distribution network is used in the tests, and test results have shown that the algorithm can determine the set of optimal nondominated solutions. It allows the utility to obtain the optimal configuration of the network to achieve the best system with the lowest cost

Water distribution network optimization using maximum entropy under multiple loading patterns

This paper proposes a maximum entropy-based multi-objective genetic algorithm approach for the design optimization of water distribution networks (WDNs). The novelty is that in contrast to previous research involving statistical entropy the algorithm can handle multiple operating conditions. We used NSGA II and EPANET 2 and wrote a subroutine that calculates the entropy value for any given WDN configuration. The proposed algorithm is demonstrated by designing a six-loop network that is well known from previous entropy studies. We used statistical entropy to include reliability in the design optimization procedure in a computationally efficient way

A graph decomposition-based approach for water distribution network optimization

A novel optimization approach for water distribution network design is proposed in this paper. Using graph theory algorithms, a full water network is first decomposed into different subnetworks based on the connectivity of the network's components. The original whole network is simplified to a directed augmented tree, in which the subnetworks are substituted by augmented nodes and directed links are created to connect them. Differential evolution (DE) is then employed to optimize each subnetwork based on the sequence specified by the assigned directed links in the augmented tree. Rather than optimizing the original network as a whole, the subnetworks are sequentially optimized by the DE algorithm. A solution choice table is established for each subnetwork (except for the subnetwork that includes a supply node) and the optimal solution of the original whole network is finally obtained by use of the solution choice tables. Furthermore, a preconditioning algorithm is applied to the subnetworks to produce an approximately optimal solution for the original whole network. This solution specifies promising regions for the final optimization algorithm to further optimize the subnetworks. Five water network case studies are used to demonstrate the effectiveness of the proposed optimization method. A standard DE algorithm (SDE) and a genetic algorithm (GA) are applied to each case study without network decomposition to enable a comparison with the proposed method. The results show that the proposed method consistently outperforms the SDE and GA (both with tuned parameters) in terms of both the solution quality and efficiency. © 2013. American Geophysical Union. All Rights Reserved.Feifei Zheng, Angus R. Simpson, Aaron C. Zecchin, and Jochen W. Deuerlei

Preconditioning water distribution network optimization with head loss-based design method

This is the author accepted manuscript. The final version is available from ASCE via the DOI in this recordThis paper develops a new domain knowledge-based initial design method for optimization of water distribution network design. The new initial water distribution network design method, termed as Headloss-based Design Preconditioner (HDP), is based on headloss analysis in the supplying path from source to user. The new HDP-preconditioned search is compared with two algorithms: one preconditioned on a velocity-based initial design method and a simple genetic algorithm without preconditioning. The results show the HDP headloss-based method outperforms the Prescreened Heuristic Sampling Method (PHSM) in terms of the quality of the initial solutions and computational efficiency on all three cases. HDP also outperforms stochastic initialization on two of the three cases. The results obtained imply that the proposed domain knowledge-based design method HDP would be able to also provide effective starting conditions for other optimization algorithms besides genetic algorithm for large water distribution systems since most optimization methods are greatly assisted by a good starting conditio

A phased approach to distribution network optimization given incremental supply chain change

Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division; in conjunction with the Leaders for Global Operations Program at MIT, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 59-60).This thesis addresses the question of how to optimize a distribution network when the supply chain has undergone an incremental change. A case study is presented for Company A, a major global biotechnology company that recently acquired a new manufacturing facility in Ireland. Company A already has international operations throughout Europe and the rest of the world through its network of 3rd party logistics providers, wholesalers, and distributors, as well as its own Benelux-based international distribution center. It now seeks to optimize its current network by taking into consideration the possibility of distributing product directly out of Ireland and by potentially outsourcing some of the distribution currently sourced from its Benelux facility. The thesis uses a phased approach to optimizing the network in order to tackle the common enterprise challenges of 1) building consensus around the solution and 2) simultaneously learning about the problem while attempting to solve it in order to meet a compressed project schedule. Through a number of simplifications, the thesis reduces the problem scope to a level that both enables the use of this phased approach and provides for a less-complex and less time-intense analysis manageable within the given time frame. The unique characteristics of the biotechnology industry drive the analysis to closely study direct effects of and potential risks to availability and lead-time of the various distribution options while trading off distribution, packaging, inventory, and capital expenditure costs. The recommendations resulting from the analysis described in this thesis are used to inform Company A's future distribution strategy regarding additional warehousing capacities, the continued use of the Benelux facility, as well as potential strategic partnerships with 3rd party logistics service providers.by Patrick Riechel.S.M.M.B.A