Network Cost Optimization Research Articles

Integral techno-economic design & operational optimization for district heating networks with a Mixed Integer Linear Programming strategy

The Netherlands, with over 90% of homes heated by natural gas is currently in the early phases of the heat transition to find alternative solutions towards 2050. According to ambitions of the Dutch government up to 50% of future implemented heating systems in the built environment will use District Heat Networks. The investment-heavy nature for District Heating System (DHS) makes it challenging to establish viable business-cases as supply-side parties require security of demand. However, resident participation is lacking as there is no integral estimate for the cost of heat over the lifetime in the early planning phase. This paper proposes a network integral techno-economic optimization with minimal a-priori assumptions. An integral network cost optimization enables to achieve a considerably more reliable cost of heat in the early planning phase. Both the investments and operational strategy are optimized with a Mixed Integer Linear Programming approach that captures the physics as well as the financial choices. Linearization of the physics are chosen to have a conservative impact on the costs estimates. The end scenario network is designed where the placement and size of sources, storage components and pipes are optimized together with the operational strategy, e.g. thermal allocation time-series, for all assets. The workflow was applied to a greenfield network for the Dutch municipality of Rijswijk. It was shown that a 18% reduction in Total Cost of Ownership in the primary grid could be achieved by introducing decentralized sources, decentralized storages and seasonal storage.

Sustainable sourcing strategy for mega construction projects: An empirical analysis

AbstractTime and cost overruns are the prominent issues faced by mega construction projects. To minimize cost overruns for the projects, a stepwise approach is suggested for the formulation of a sustainable sourcing strategy. Initially, weights are derived for the existing suppliers using the analytic hierarchy process to select the best set of suppliers. Multiple conflicting variables are considered for deriving the weights. Based on the derived weights, the supply network cost optimization model is developed for optimal order quantity allocation to the selected suppliers to maximize total purchasing value (TPV). A multi‐objective weighted goal programming model is used to minimize deviations in the TPV. Finally, the full factorial design of the experiment concept is employed to identify the significant factors impacting deviations in TPV. The methodology developed is verified using a case study approach. Mega‐construction project organization case was considered for analysis. Demand for the product, materials procurement cost, quality, and supplier lead time are found to be the significant factors impacting deviations in TPV. The developed approach can provide valuable insights to practitioners for the formulation of a sustainable sourcing strategy.

Exploring N-best solution space for heat integrated hydrogen regeneration network using sequential graph-theoretic approach

To achieve the ever-stringent sustainable goals, this paper aims to synthesize a heat integrated hydrogen regeneration network (HIHRN) using a graph-theoretic-based sequential method. Firstly, the optimal and near-optimal structures for a hydrogen regeneration networks (HRN) are determined using P-graph model with consideration of both impurity and pressure constraints. These networks are then used as inputs in P-HENS software to generate a list of optimal and near-optimal heat exchanger network (HEN) structures. An eight source and sink problem is used to demonstrate the effectiveness of the proposed method. There are 199,677 feasible HIHRN structures identified, while the 6 near-optimal solutions which are within 0.05% tolerance of the optimal network cost (i.e., less than 33.04 M$/y) are presented together with the top four HEN designs that can offer comparable costs (∼115,500 $/y). In addition, the impacts of pressure swing adsorber (PSA) pressure drop consideration and minimum temperature difference on the optimal design are also presented.

MLCOR Model for Suppressing the Cascade of Edge Failures in Complex Network

As a decisive parameter of network robustness and network economy, the capacity of network edges can directly affect the operation stability and the construction cost of the network. This paper proposes a multilevel load–capacity optimal relationship (MLCOR) model that can substantially improve the network economy on the premise of network safety. The model is verified in artificially created networks including free-scale networks, small-world networks, and in the real network structure of the Shanghai Metro network as well. By numerical simulation, it is revealed that under the premise of ensuring the stability of the network from the destruction caused by initial internal or external damage on edge, the MLCOR model can effectively reduce the cost of the entire network compared to the other two linear load–capacity models regardless of what extent of the destruction that the network edges suffer initially. It is also proved that there exists an optimal tunable parameter and the corresponding optimal network cost for any BA and NW network topology, which can provide the reference for setting reasonable capacities for network edges in a real network at the stage of network planning and construction, promoting security and stability of network operation.

Optimization of district heating network design

Due to the relatively high investment costs of district heating networks, their optimal design is crucial for a high ROI. A software tool supporting decision makers and network planners to find the cost optimal network topology is introduced. Beside topology the tool also estimates diameters of the district heating pipes. The optimization is based on user defined cost data, geo-referenced input of supply and consumer nodes, all possible laying routes for the pipes and technical boundary conditions. Further constraints can be considered, e.g., the usage of an already existing network or possible obstacles, e.g., costly crossings of rivers or railroad tracks. The optimization usually leads to an unmeshed network. Meshes which are needed to guarantee uninterrupted service can only be enforced by the manually definition of existing pipes. To ensure an easy-to-use interface for geo-referenced data the tool is provided as a plugin for the geographic information system QGIS.The optimization process consists of a combination of classical non-linear methods, minimal spanning tree calculations and evolutionary algorithms. It allows solution of the mentioned problem for one specific user defined design case. In addition to the geo-referenced topology of the network, the diameter of each pipe and the total investment costs, the result of the computation provides further technical specifications, e.g., mean flow velocities and pressure losses, in the considered case. So far, different scenarios can only be calculated separately.With a short training period the tool is largely intuitive to use. It is currently used in university courses in order to understand the challenges involved in the planning and design process of district heating networks. It is also used in research projects and is continuing to be developed.

Cost Optimal Sizing of Hybrid Power Systems: A Pinch Analysis Approach

Technologies related to the advancement of renewable energy are growing very fast. Development in renewable energy technologies, reliability, and environmental restrictions generates the requirement of a hybrid power system (HPS). Proper financial management is necessary to ensure the profitability and sustainability of any project. So, while planning a HPS an essential factor that should be taken into account is cost. This factor can be included in planning a HPS via pinch analysis through the concept of prioritization. This concept is a well-established technique in Process Integration for designing cost-optimal networks for recovery and conservation of resources such as heat, mass, water, carbon, gas, properties and batch, production. However, its application in planning HPSs still needs development. This paper extends the concept of prioritization for planning HPS to develop cost-optimal HPS. The cost of power generation depends on the type of power resource. The time of availability of each source is also taken into account. The methodology presented in this paper calculates the power rating of resources required for satisfying the power demands with the objective to minimize the overall cost of electricity in HPS.

Bioreactor Simulation with Quadratic Neural Network Model Approximations and Cost Optimization with Markov Decision Process

The efficient operation of activated sludge type wastewater treatment plants is an ongoing topic for the utility providers, where electric energy consumption shares are high, giving cca. 30 % of total operational costs. Intervention methods for intensification include fine tuning of aeration settings, sludge removal and the adjustment of recirculation rates. In order to analyze the effects of various process control strategies, activated sludge models (ASM) are used for the purpose of biokinetic modeling. In practice, most model simulators do not incorporate optimization and necessary auto-calibration of the latter, due to high computational demand of timeseries evaluation. In this paper, a new mathematical model is presented, which makes biokinetic simulations suitable for the use in decision support systems. Namely, the ASM model is approximated with a computanional inexpesive quadratic model solution, fed into a set of mass-balance corrected neural networks. Cost optimization is achieved with Markov decision process model. The developed method was illustrated for a case of Hungarian, large wastewater treatment plant. It was proven, the model is able to find better aeration schemes for the plant in aspect of cost of operation and nitrogen removal efficiency. The model can be used to find cost-optimal policies under arbitrary defined conditions. As a benefit, results can be implemented into industrial logic controllers.

Reporting cell planning-based cellular mobility management using a Binary Artificial Bat algorithm

This paper attempts to present a novel application of Binary Artificial Bat algorithm for more effective location management in cellular networks. The location management is a mobility management task, which involves tracking of the mobile stations to locate their exact positions so that an incoming call or data can be routed to the intended mobile user. The location management cost comprises of the costs incurred by two processes, namely location registration and location search. This work focuses on network cost optimization, using Binary Artificial Bat algorithm for reporting cell planning strategy, which has not been reported yet. Results of the proposed algorithm have been compared with that of Binary Particle Swarm Optimization (BPSO) and Binary Differential Evolution (BDE) for some reference and realistic networks. The proposed approach is found to perform as good as other state-of-art techniques reported in the literature in terms of accuracy in solution, but it shows perceptible improvement in convergence speed.

Optimizing Network Slice Dimensioning via Resource Pricing

Network slicing has been viewed as a key enabler for the next-generation software-defined and cloud-based network ( e.g. , 5G and beyond) to accommodate diverse services in a flexible and cost-efficient fashion. Network slicing allows a network slice provider (NSP) to operate on a common network infrastructure to create customized isolated logical networks ( i.e. , network slices) for network slice customers (NSCs), ( i.e. , service providers). NSP and NSCs are independent operators who pursue profit maximization, while in the literature, only network cost optimization is intensively investigated in terms of service function chain embedding, i.e. , virtual network function (VNF) placement and flow routing. Therefore, slices should be dimensioned ( i.e. , resources allocated to slices) according to the resource availability and the economic mechanism in the network, so as to optimize the resource utilization and improve the profit of NSP/NSCs. In this paper, we study elastic slice dimensioning with resource pricing as a Stackelberg pricing game, in which the NSP sells slices by pricing resources and NSCs adjust their slice’s resource demand on VNF capacity and bandwidth, while both are trying to maximize their profit. Then, we formulate optimization problems for the pricing game and find that a closed form solution of the optimal price cannot be obtained for a non-trivial network. Hence, we propose a resource pricing algorithm that aims to maximize the NSP’s profit and the network’s social welfare. Compared with existing usage-based pricing method and two heuristic methods, our proposed pricing algorithm for slice dimensioning strikes a trade-off between maximizing NSP’s profit and other metrics, including the resource utilization. Hence, it will helpfully exploiting the benefits of network slicing.

Optimal Design of Switched Networks of Positive Linear Systems via Geometric Programming

In this paper, we propose an optimization framework to design a network of positive linear systems whose structure switches according to a Markov process. The optimization framework herein proposed allows the network designer to optimize the coupling elements of a directed network, as well as the dynamics of the nodes in order to maximize the stabilization rate of the network and/or the disturbance rejection against an exogenous input. The cost of implementing a particular network is modeled using posynomial cost functions, which allow for a wide variety of modeling options. In this context, we show that the cost-optimal network design can be efficiently found using geometric programming in polynomial time. We illustrate our results with a practical problem in network epidemiology, namely, the cost-optimal stabilization of the spread of a disease over a time-varying contact network.

OPTIMIZATION OF WATER SUPPLY NETWORKS CONSIDERING RELIABILITY BY USING THE LEAST VARIANCE DISCHARGES

A water supply network should be capable to meet the consumers demand, in normal and abnormal flow situations, with proper pressure in all parts of the network. This research aims to find a method in which a network is designed to meet the customer need with adequate pressure in addition to the lowest cost, in fact the aim of network cost optimization with regard to the reability. In order to apply reliability, uniformity of flow distribution network pipes is considered and to create this scale, the concept of Discharge variance of pipes is used. Optimization using genetic algorithms has been done, and its cost function, in addition to the investment and annual energy costs per year, includes the expected downtime and repair costs per year. Reliability is checked in two modes; in the first mode, only operational state of the network pipes is investigated; while in the latter mode, the removal of individual pipes as well as the overall operational state was considered. Since in the state of removed pipes, EPANET software is not able to do correct analysis, its modified version is used which is called EPANET + Water Net Gen. The comparison of results shows that, in general, use of this definition provides high levels of reliability.

Hybrid optimization and simulation to design a logistics network for distributing perishable products

Dynamics in product quality complicate the design of logistics networks for perishable products, like flowers and other agricultural products. Complications especially arise when multiple products from different origins have to come together for processes like bundling. This paper presents a new MILP model and a hybrid optimization–simulation (HOS) approach to identify a cost-optimal network design (i.e. facility location with flow and process allocation) under product quality requirements. The MILP model includes constraints on approximated product quality. A discrete event simulation checks the feasibility of the design that results from the MILP assuming uncertainties in supply, processing and transport. Feedback on product quality from the simulation is used to iteratively update the product quality constraints in the MILP. The HOS approach combines the strengths of strategic optimization via MILP and operational product quality evaluation via simulation. Results, for various network structures and varying degrees of dynamics and uncertainty, show that if quality decay is not taken into account in the optimization, low quality products are delivered to the final customer, which results in not meeting service levels and excess waste. Furthermore, case results show the effectiveness of the HOS approach, especially when the change from one iteration to the next is in the choice of locations rather than in the number of location. It is shown that the convergence of the HOS approach depends on the gap between the product quality requirements and the quality that can be delivered according to the simulation.

An analytical approach for estimating optimal multi-layer network cost, exploiting the flexible-grid optical technology

In this paper we provide analytical insights for dimensioning a multi-layer network and in particular the virtual optical transport network topology exported to the packet client layer according to cost and power consumption. A new approach is described, approximating optimum routing, that can be effectively used to provide an estimation of network cost and reference architectures in network implementations. Results using this estimation approach are compared with those obtained using other estimation approaches regarding bandwidth allocation and network costs. We found that the approximated routing calculations, applied on a virtual network topology, do not significantly penalize the accuracy of the results. This approach can be extended to analyze the impact of new a optical networking technique, flexible-grid wavelength division multiplexing (WDM), providing some insights regarding scenarios in which migration from fixed-grid WDM systems to flexible-grid WDM systems would be advantageous in terms of capital cost and power consumption.

Understanding the robustness of optimal FMCG distribution networks

Distribution network design is about recommending long-term network structures in an environment where logistic variables like transportation costs or retailer order sizes dynamically change over time. The challenge for management is to recommend an optimal network configuration that will allow for longer term optimal results despite of environmental turbulences. This paper studies the robustness of cost-optimized FMCG (fast-moving consumer goods) distribution networks. It aims at observing the impact of changing variables/conditions on optimized logistic structures in terms of the optimal number and geographical locations of existing distribution centers. Five variables have been identified as relevant to the network structure. A case study approach is applied to study the robustness of an existing, typical, and optimized FMCG network. First, distribution network data of a German manufacturer of FMCG are recorded and analyzed. A quantitative model is set up to reflect the actual cost structure. Second, a cost optimal network configuration is determined as a benchmark for further analysis. Third, the variables investigated are altered to represent changes, both isolated (ceteris paribus) and in combination (scenario analysis). Each one of the variables investigated proves to be fundamentally able to suggest a change of the optimal network structure. However, the scenario analysis indicates that the expected changes will by and large compensate each other, leaving the network in near optimal condition over an extended period of time.

Heat exchanger network cost optimization considering multiple utilities and different types of heat exchangers

Supertargeting based on composite curves (CC) is widely used to determine the optimum approach temperature (ΔTmin) that yields the minimum total cost for heat exchange networks (HEN). Supertargeting using CC has two key limitations. Firstly, the HEN area calculations are drastically simplified through the assumption that CC segments may be considered as pseudo-single hot and cold streams exchanging heat via only one exchanger that is governed by a single cost correlation. Secondly, the current Supertargeting approach of considering only one hot and one cold utility level may lead to a crude estimation of the total HEN cost and the optimum ΔTmin. This work presents the stream temperature vs. enthalpy plot supertargeting (STEPS) method that overcomes these limitations. This paper proves that supertargeting based on CC can lead to up to 50% error in the total cost target and poor ΔTmin estimations.

Water network cost optimization in a paper mill based on a new library of mathematical models

The increasing costs associated with water supply and the disposal of wastewater has stimulated industries to seek more efficient water management systems. Mathematical modelling and simulation can be a very valuable tool for the study of the multiple alternatives available whilst assessing optimum solutions for water management in industry. This study introduces a new steady state model library able to reproduce industrial water circuits. It has been implemented in a novel software framework for the representation, simulation and optimization of industrial water networks. A water circuit representing a paper mill has been modelled and simulated showing the capability to reproduce real case studies. Alternative scenarios for the water network have also been tested to assess the capability of the models to optimize water circuits minimizing total cost.

A Modeling Method for Router-Level Topologies based on Network-Cost Optimization

Measurement studies of Internet topologies show that the degree distribution of the topologies exhibits a power-law attribute. However, it is apparent that only degree distributions do not determine the structure of ISP topologies, where ISP designs routerlevel topologies based on their own design policies. Other structural properties than degree distribution are important to generate realistic Internet topologies. In this paper, we develop a modeling method for generating realistic ISP Internet topologies that obey a power-law degree distribution and have similar structural properties observed in the measurement studies. Our modeling method adds nodes one by one, and each node connects to optimal nodes to minimize overall network-cost. Then we answer the following question: what design factors are important to form realistic Internet topologies? Our results show that the answers are node locations and traffic demands.

Optimizing the Migration of Channels With Higher Bitrates*

Increasing the capacity of optical networks raises the issue of cost optimal bitrate migration into existing infrastructures. In this paper, we use the migration from 10 G to 40 G services as an example. 10 G demands can be groomed into 40 G channels or transmitted natively, reducing reach of concurrently mixed 40 G channels. We study the migration scenario from a networking point of view, optimizing the migration strategy in terms of capital expenditures. For a lower and an upper cost bound, we apply the two multiperiod planning approaches: all-periods planning and incremental planning, respectively. The migration optimization shows considerable impact from forecast knowledge to cost optimality, channel mix, and aggregation decisions. The impact of the topology dimension on migration strategy and overall costs are investigated for a USA and a British network. For long-term migration under traffic growth, optimal network cost is achieved by early investments in 40 G-only equipment - a conclusion that applies to both network topologies.

Optimizing the Migration of Channels With Higher Bitrates

Increasing the capacity of optical networks raises the issue of cost optimal bitrate migration into existing infrastructures. In this paper, we use the migration from 10G to 40G services as an example. 10G demands can be groomed into 40G channels or transmitted natively, reducing reach of concurrently mixed 40G channels. We study the migration scenario from a networking point of view, optimizing the migration strategy in terms of capital expenditures. For a lower and an upper cost bound, we apply the two multiperiod planning approaches: all-periods planning and incremental planning, respectively. The migration optimization shows considerable impact from forecast knowledge to cost optimality, channel mix, and aggregation decisions. The impact of the topology dimension on migration strategy and overall costs are investigated for a USA and a British network. For long-term migration under traffic growth, optimal network cost is achieved by early investments in 40G-only equipment-a conclusion that applies to both network topologies.

Reduction techniques for the PNS problems: a novel technique and a review

Process Network Synthesis (PNS) has an enormous practical impact. The problem is very difficult to solve, determining the cost optimal network of operating units with fixed charge belongs to the complexity class of NP-hard problems. Therefore, it is important to develop reduction algorithms to minimize the size of the problem. In the present work the available reduction techniques for PNS problems are reviewed as well as a further reduction algorithm is presented. The performance of the new algorithm is examined by empirical analysis.