Distribution Network Design Research Articles

Distributed power supply and energy storage configuration method in the active power distribution network

Distributed energy access and energy storage configuration are important links in the design of an active distribution network, and research on their design methods is essential to support the scientific development of an active distribution network. Therefore, on the basis of full research and demonstration, the content of the scheme design of the active distribution network and the technical principle of the overall design of the active distribution network are first determined, so that they can play the role of macro instruction in the scheme design. In addition, this paper deeply studies the capacity allocation of distributed generation and energy storage systems in the active distribution network, puts forward a general method for optimal allocation of distributed generation access and energy storage capacity, and provides design information and technical support for the active distribution network method.

A new multi-objective evolutionary algorithm for the optimization of water distribution networks

Abstract This work introduces a new bi-objective optimization model for the design of a water distribution network (WDN). Reliability and design cost are two contradicting objectives that are optimized. The evolutionary technique multi-objective (MO) Jaya, hereafter referred as MO-JAYANET, is developed for the optimal design of WDN. EPANET is used as a hydraulic network solver in this study. The concept of constraint violation, dominance, non-dominance and crowding distance is used to develop MO-JAYANET. As the Jaya algorithm is a parameter-less technique, and with the concept of constraint dominance involved in the MO model, the penalty parameter required to penalize the non-feasible solution is also dropped. This makes MO-JAYANET a completely parameter-less technique, requiring no tuning at all. MO-JAYANET is applied on two benchmark networks from the literature using three different reliability indices that are Resilience Index, Network Resilience Index, and Modified Resilience Index. MO-JAYANET is found to be very easy and efficient for WDN optimization. The present work obtains a smooth Pareto Front (PF) that overlaps the best-known PF. The maximum reliability diameters obtained by the MO-JAYANET for the three indices are also simulated for both hydraulic and mechanical failures under different scenarios to identify a better-performing index.

Analysis of Water Distribution Network Using Epanet for Normal and Leakage Condition and Its Effect on Pressure

Abstract: Water distribution network is a system of engineered hydrologic and hydraulic component which supplies water from the source to the required area. In the design of water distribution network, it is crucial that the network supplies the forecasted demands with enough residual heads at all nodes of the network during the entire design period. However with the increasing change in future scenario, either the nodal demands change or the water distribution network falls short to meet the increasing demands. Therefore, we require a proper analysis of water networks to strengthen them for future. In situations like this, softwares play a major role in analyzing water distribution network to make it work as per the design. The present study is an effort to analyze the water supply network using available software, it proposes hydraulic simulation model for water distribution network analysis and comparison of results.

Pricing quantitative model design of distribution network assets REITs products and ABS products

Abstract In recent years, the development of asset securitization in China has maintained a rapid growth pattern. The raised funds will be used to support enterprises in power grids or related industrial chains to obtain financial equivalent services. Reasonable pricing of distribution network assets REITs, ABS products and other financial products related to distribution network assets is an important auxiliary means to establish asset guarantee. The research of quantitative distribution network asset valuation model provides a good theoretical and working support for solving the reasonable pricing problem of distribution network assets. This paper will focus on the theoretical basis and related methods of quantitative model design for REITs products and ABS products from the perspective of distribution network assets. The results show that asset prices fluctuate at the 1% level for every 1% change in default rate when the prepayment rate is unchanged. The results show that the price of ABS products is more sensitive to the change of prepayment rate.

Multi-period distribution network design with boundedly rational customers for the service-oriented manufacturing supply chain: a 4PL perspective

With the service level playing an increasingly essential role in the service-oriented manufacturing (SOM) supply chain, the distribution network design can be heavily affected by the customer behaviour based on their satisfaction of services. In this paper, we consider the service level for service time and delivery quantity separately. A novel mixed integer non-linear programming model is proposed to design the multi-period distribution network from a fourth-party logistics (4PL) perspective. The customer satisfaction based on prospect theory is maximised while considering the investment budget and the service level. A scenario-based linear reformulation is proposed to find the optimal solution when the problem scale is small. For a large-scale problem, we propose an individual-driven Q-learning based memetic particle swarm optimisation algorithm. Numerical experiments are conducted to demonstrate the effectiveness and efficiency of the proposed algorithm. Furthermore, the impact of service modes, different customer behaviour, and customer satisfaction evaluation periods on distribution network is investigated. We find that the length of evaluation periods leads to differences in customer satisfaction due to different perceptions of ‘small loss’ and ‘big gain’ by boundedly rational customers.

Application of Fine-Tuned Krill Herd Algorithm in Design of Water Distribution Networks

Stochastic population-based metaheuristic algorithms have been widely used to design water distribution networks (WDNs). The present study introduces one of its kind, the krill herd algorithm (KHA), a novel swarm intelligence-based metaheuristic. Demonstrating KHA application to WDN design, the study proposes its enhanced version to improve its convergence precision. Consequently, combining the fine-tuned mechanism of KHA with the EPANET 2.2 simulation software, the fine-tuned KHA (FIT-KHA) model is formulated and checked for its efficiency in handling the WDN design problem. Because three benchmark problems (new and rehabilitation WDNs) of different dimensions are considered, the significance of the algorithm control parameters concerning the network size is studied. From sensitivity analysis, the time step factor, number of krill, and maximum iteration size are found to influence the algorithm performance. Their optimal values followed an increasing trend with the network size. The computational results reinforce a superior search exploitation ability of the FIT-KHA. Importantly, the results exhibit a better computational efficiency of KHA over most of the reported metaheuristic algorithms. Then, the performance state of optimally designed WDN under hydraulic (demand, roughness coefficient variations) and mechanical (single- and two-pipe failure) uncertain scenarios is studied. Besides reliability and surrogate measures, multiaspect metrics based on adequacy and equity are evaluated to assess the network’s performance. The WDNs are observed to be more susceptible to demand uncertainties over roughness coefficient variation. At higher states of mechanical (pipe) failure scenarios, especially, the equity of the WDN is disturbed compared to the adequacy, except for rehabilitated WDN. Certainly, the performance study manifests that the reliability measure that truly presents the WDNs performance is the function of network type. While proposing KHA as an effective alternative optimization tool, the study suggests a prior performance study to choose an appropriate metric to formulate a reliability-based multiobjective framework for the robust design of WDNs.

Chaotic differential evolution algorithms for optimal design of water distribution networks

ABSTRACT Recently, coupling chaos theory with evolutionary algorithms (EAs) elevated further scope for improving EAs’ performance efficiency. In this view, the present study emphasizes investigating the influence of chaotic force on convergence properties of differential evolution (DE) algorithm in designing water distribution networks (WDNs). To this end, two novel chaos-directed DE models, Chaotic-DE and Chaotic-Fm-DE, are proposed. The Chaotic-DE model is formulated to enhance DE’s searchability and faster convergence by replacing every random phenomenon with a chaotic force. The Chaotic-Fm-DE model with a dynamic, chaotic mutation factor is developed to improve DE’s exploitation behavior. Essentially, these models differ from the previous chaos-directed EA models in how chaos ergodicity is simulated in DE mechanism. A novel scheme of non-sequential approach is used for this purpose. Further, their respective elitist models are formulated to promote the search in promising areas. Importantly, the elitism scheme developed saves the elite trial vectors to pass through the next generations. The results of proposed algorithms validated on five (new and rehabilitated) benchmark WDNs (whose dimensions vary from 8 to 454) demonstrate the enhanced search behavior of the chaotic models with solution precision and remarkable reduction in computational effort over the non-chaotic ones.

QuCTS—Single-Flux Quantum Clock Tree Synthesis

Superconductive rapid single-flux quantum (RSFQ) is an emerging cryogenic technology, promising a significant boost in performance and ultralow power consumption. The operating frequency achieved by RSFQ digital integrated circuits is several orders of magnitude greater than traditional CMOS circuits. The fundamental difference of RSFQ circuits, however, renders traditional clocking techniques appropriate for CMOS unsuitable for RSFQ technology. Most RSFQ logic gates, such as AND and OR, are sequential in nature. The number of pipeline stages is therefore significantly greater in RSFQ as compared to CMOS, complicating the clock distribution network design process. This issue is further exacerbated with the need for splitters to achieve a fanout greater than one and the need for transmission lines rather than ordinary metallic wires as in CMOS. In this work, QuCTS—single-flux Quantum (SFQ) Clock Tree Synthesis—is presented. QuCTS utilizes a two-stage framework for synthesizing clock networks. In the clock skew scheduling stage, the clock signal arrival time of each gate is chosen to maximize the robustness of the circuit to timing variations. In the clock tree synthesis stage, the layout of the clock distribution network is generated based on a novel delay equilibration technique. QuCTS is the first clock tree synthesis tool for RSFQ circuits utilizing useful clock skew. The synthesized network satisfies the clock arrival time requirements while minimizing the associated overhead, such as the interconnect length and number of delay elements. The tool is validated on a set of benchmark circuits. In a prototypical case study, a clock tree is generated for the AMD2901 with 1049 clock sinks in 53 min while satisfying the clock arrival time.

A Chaotic Sobol Sequence-based multi-objective evolutionary algorithm for optimal design and expansion of water networks

The design of a water distribution network (WDN) is an optimization problem that is computationally challenging with conflicting objectives. This study offers an enhanced Chaotic Sobol Sequence-based Multi-Objective Self-Adaptive Differential Evolution (CS-MOSADE) algorithm for multi-objective WDN design. The CS-MOSADE algorithm was tested on two benchmark WDNs, and a real WDN. Optimization results indicate that the CS-MOSADE algorithm converged two to three times faster than the MOSADE and NSGA-IIalgorithms and led to better output in terms of even distribution of solutions and convergence towards the true Pareto-optimal front. Smaller spacing metric indicated better uniformity in the obtained solutions; and larger hyper-area and coverage function values depicted better convergence towards the true Pareto-optimal front for the CS-MOSADE algorithm compared to the other algorithms. The CS-MOSADE algorithm was then applied to solve a WDN expansion problem for optimal pump scheduling and minimization of Life Cycle Cost, maximization of reliability and minimization of Green House Gas (GHG) emissions. A significant reduction in GHG emissions of 2.17 x 106 kg was achieved at an additional cost of $0.55 x 107 when optimal pump scheduling was incorporated in the model of the real WDN over service life of 50 years.

E-grocery logistics: exploring the gap between research and practice

PurposeThis paper investigates the logistics management in the e-grocery sector. It contrasts the key issues faced by practitioners and the topics addressed in the academic literature, to identify potential misalignments between research and practice and propose avenues for future efforts.Design/methodology/approachThis work adopts a twofold methodological approach. From an academic perspective, a systematic literature review (SLR) is performed to define the topics addressed so far by scholars when analysing e-grocery logistics. From a managerial perspective, a Delphi study is accomplished to identify the most significant issues faced by logistics practitioners in the e-grocery context and the associated significance.FindingsThe study develops a conceptual framework, identifying and mapping the 9 main logistics challenges for e-grocery along 4 clusters, in the light of a logistics-related revision of the SCOR model: distribution network design (area to be served, infrastructures), order fulfilment process (picking, order storage, consolidation, delivery), logistics-related choices from other domains (product range, stock-out management) and automation. These elements are discussed along three dimensions: criticalities, basic and advanced/automation-based solutions. Finally, the main gaps are identified – in terms of both under-investigated topics (order storage and stock-out management) and investigated topics needing further research (picking and automation) – and research questions and hypotheses are outlined.Originality/valueThis paper provides a threefold contribution, revolving around the developed framework. First, it investigates the state of the art about e-grocery logistics, classifying the addressed themes. Second, it explores the main issues e-grocery introduces for logistics practitioners. Third, it contrasts the two outcomes, identifying the misalignment between research and practice, and accordingly, proposing research directions.

Discussion on Energy-saving Measures in 10kV Distribution Network Design

Discussion on Energy-saving Measures in 10kV Distribution Network Design

Power distribution network design considering dynamic and differential pricing, buy-back, and carbon trading

The increase in energy demand and the effect of traditional energy generation supported by the penetration of technological advancements in renewable energy generation calls for an integrated power distribution network design. In this study, a power distribution network design with the establishment of distributed renewable energy resources and involvement of prosumers in energy system to generate, buy, and supply carbon-free energy is considered. The dynamic prices that cope with the dynamic demand of both prosumers and customers over the energy planning time horizon and the buy-back price to be paid to the prosumers are also considered. A mathematical model for power distribution network design, including carbon trading and a buy-back policy, was developed. The objective is to determine the generation capacity of the distributed renewable energy resources to be installed at different potential sites, the differential prices offered to prosumers and consumers, and the buy-back price while maximizing the profit of a power plant. A scenario-based stochastic solution approach and fuzzifying fuzzy parameters are provided to address the above problem. The results show that the power plant earns the most profit at a relation factor of 0.8 between the differential prices by offering the highest buy-back price to incentivize the prosumers. With the increase in the relation factor, the net profit of the power company decreases, but the buy-back price increases, while the dynamic prices remain unpredictable. The average buy-back price offered to prosumers was 62.4% of the average selling price.

Instant flow distribution network optimization in liquid composite molding using deep reinforcement learning

Carbon fibre reinforced plastic (CFRP) manufacturing cycle time is a major driver of production rate and cost for aerospace manufacturers. In vacuum assisted resin transfer molding (VARTM) where liquid thermoset resin is infused into dry carbon reinforcement under vacuum pressure, the design of a resin distribution network to minimize fill time while ensuring the preform is completely full of resin is critical to achieving acceptable quality and cycle time. Complex resin distribution networks in aerospace composites increase the need for quick, optimized virtual design feedback. Framing the problem flow media placement in terms of reinforcement learning, we train a deep neural network agent using a 3D Finite Element based process model of resin flow in dry carbon preforms. Our agent learns to place flow media on thin laminates in order to avoid resin starvation and reduce total infusion time. Due to the knowledge the agent has gained during training on a variety of thin laminate geometries, when presented with a new thin laminate geometry it is able to propose a good flow media layout in less than a second. On a realistic aerospace part with a complex 12-dimensional flow media network, we demonstrate our method reduces fill time by 32% when compared to an expert designed placement, while maintaining the same fill quality.

Social equity-based distribution networks design for the COVID-19 vaccine

This study aims to investigate the role of social equity in vaccine distribution network design problems. Inspired by the current COVID-19 vaccine allocation in-country context, we capture social equity-based distribution by modeling three theories: Rawls’ theory, Sadr’s theory, and utilitarianism. We consider various social groups based on degree of urbanization, including inhabitants of cities, towns and suburbs, and rural areas. The distribution problem is subject to, on the one hand, demand-side uncertainty characterized by the daily contamination rate and its space–time propagation that anticipate the in-need population. On the other hand, supply-side uncertainty characterized by the stochastic arrival of vaccine doses for the supply period. To tackle this problem, we propose a novel bi-objective two-stage stochastic programming model using the sample average approximation (SAA) method. We also develop a lexicographic goal programming approach where the social equity objective is prioritized, thereafter reaching an efficiency level. Using publicly available data on COVID-19 in-country propagation and the case of two major provinces in Iran as example of middle-income country, we provide evidence of the benefits of considering social equity in a model-based decision-making approach. The findings suggest that the design solution produced by each social equity theory matches its essence in social science, differing considerably from the cost-based design solution. According to the general results, we can infer that each social equity theory has its own merits. Implementing Rawls’ theory brings about a greater coverage percentage in rural areas, while utilitarianism results in a higher allocation of vaccine doses to social groups compared to the Sadr and Rawls theories. Finally, Sadr’s theory outperforms Rawls’ in terms of both the allocation and cost perspective. These insights would help decision-makers leverage the right equity approach in the COVID-19 vaccine context, and be better prepared for any pandemic crisis that the future may unfold.

Fundamental Points of Power Distribution Network Design on Printed Wiring Board —Target Impedance and PI Validation—

Fundamental Points of Power Distribution Network Design on Printed Wiring Board —Target Impedance and PI Validation—

Bi‐objective optimization of a stochastic resilient vaccine distribution network in the context of the COVID‐19 pandemic

This paper develops an approach to optimize a vaccine distribution network design through a mixed-integer nonlinear programming model with two objectives: minimizing the total expected number of deaths among the population and minimizing the total distribution cost of the vaccination campaign. Additionally, we assume that a set of input parameters (e.g., death rate, social contacts, vaccine supply, etc.) is uncertain, and the distribution network is exposed to disruptions. We then investigate the resilience of the distribution network through a scenario-based robust-stochastic optimization approach. The proposed model is linearized and finally validated through a real case study of the COVID-19 vaccination campaign in France. We show that the current vaccination strategies are not optimal, and vaccination prioritization among the population and the equity of vaccine distribution depend on other factors than those conceived by health policymakers. Furthermore, we demonstrate that a vaccination strategy mixing the population prioritization and the quarantine restrictions leads to an 8.5% decrease in the total number of deaths.

A sustainable approach for a collaborative distribution network

Sustainability has recently become a challenge for many industrial and commercial companies. However, it has become important for them to understand how sustainability performance balances with their competitive priorities. In this paper, horizontal collaboration is presented as a powerful solution to achieve this objective. It focuses on the collaborative distribution network design problem to optimize the freight transport from suppliers to customers located in the urban area. To address this problem, a mixed integer linear programming is proposed. It takes into account four dimensions of sustainability. The economic and environmental dimensions aim respectively at reducing the logistics costs and the CO2 emissions form vehicles and hubs. The social dimension aims at maximizing the created job opportunities and assessing the accident risk and the noise level. The institutional dimension is ensured by the constraints of the regulation relative to the access of vehicles according to weight and schedule. The optimization of the model is performed in a single and multi-objective way by applying the weighted sum method. Several numerical examples are carried out based on a case study of an agri-food distribution network located in Tunisia to validate the proposed model. Finally, some managerial implications are concluded.

The two-echelon stochastic multi-period capacitated location-routing problem

Given the emergence of two-echelon distribution systems in several practical contexts, this paper tackles, at the strategic level, a distribution network design problem under uncertainty. This problem is defined as the two-echelon stochastic multi-period capacitated location-routing problem (2E-SM-CLRP). It considers a network partitioned into two capacitated distribution echelons: each echelon involves a specific location-assignment-transportation schema that must cope with the future demand. It aims to decide the number and location of warehousing/storage platforms (WPs) and distribution/fulfillment platforms (DPs), and on the capacity allocated from first echelon to second echelon platforms. In the second echelon, the goal is to construct vehicle routes that visit ship-to locations (SLs) from operating distribution platforms under a stochastic and time-varying demand and varying costs. This problem is modeled as a two-stage stochastic program with integer recourse, where the first-stage includes location and capacity decisions to be fixed at each period over the planning horizon, while routing decisions of the second echelon are determined in the recourse problem. We propose a logic-based Benders decomposition approach to solve this model. In the proposed approach, the location and capacity decisions are taken by solving the Benders master problem. After these first-stage decisions are fixed, the resulting sub-problem is a capacitated vehicle-routing problem with capacitated multiple depots (CVRP-CMD) that is solved by a branch-cut-and-price algorithm. Computational experiments show that instances of realistic size can be solved optimally within a reasonable time and provide relevant managerial insights on the impact of the stochastic and multi-period settings on the 2E-CLRP.

A multi-objective optimization model for the problems of sustainable collaborative hub location and cost sharing

This paper addresses the problems of distribution network design and gain sharing in a collaborative context. We propose mathematical models to compare the performance of three scenarios based on several sustainability indicators: logistics costs, CO2 emissions, created job opportunities, noise level, and accident risk. The scenarios assume multi-period transportation planning, performed by a heterogeneous fleet of vehicles. Mathematical models are used to determine the number of hubs, their capacities, and their locations, as well as the links between network nodes, the quantities transported, the quantities delayed, the inventory level, and the number and type of vehicles used in each period. Single-objective optimization is performed in an exact manner for small instances, while the genetic algorithm is used to solve large instances. Multi-objective resolution is performed using the ε-constraint method for small instances and the non-dominated sorting genetic algorithm II for large instances. We use a distribution network located in France for our numerical experiments. The costs of collaboration were shared by three approaches: egalitarian approach, volume method, and Shapley value. Experimental results and sensitivity analysis reveal some interesting findings: (1) it is always beneficial to design a collaborative distribution network; (2) relaxing delivery times and increasing the number of available vehicles further improve collaborative performance; and (3) cooperative game-theoretic approaches are most appropriate for sharing costs.

Efficiency of Using Geoinformation Systems in the Design of Gas Distribution and Gas Consumption Networks

The scientific article is devoted to determining the effectiveness of the use of geoinformation systems (GIS) in the design of gas distribution and gas consumption networks. An experiment was carried out on a laboratory stand modeling ring, dead-end and mixed gas networks. The electronic model of a gas network identical to the laboratory stand in the ZuluGaz 10.0 software package, a module for expanding the GIS functionality for calculating gas supply networks designed to solve various industry. A comparative analysis of the results of an experiment and the results obtained during simulation in GIS is carried out. Based on the analysis, a conclusion is made about the accuracy of the GIS results and the feasibility of using GIS for the design of gas distribution and gas consumption systems.