Showing papers on "Cost reduction published in 2014"
TL;DR: In this article, a cost model for vanadium and iron-vanadium redox flow batteries is developed to estimate stack performance at various power densities as a function of state of charge and operating conditions.
351 citations
TL;DR: It is shown that the proposed design can achieve significant saving in electricity cost, allow more flexibility in setting the tradeoff between cost and user comfort, and enable to reduce energy demand during peak hours.
Abstract: In this paper, we investigate the joint optimization of electric vehicle (EV) and home energy scheduling. Our objective is to minimize the total electricity cost while considering user comfort preference. We take both household occupancy and EV travel patterns into account. The novel contributions of this paper lie in the exploitation of EVs as dynamic storage facility as well as detailed modeling of user comfort preference, thermal dynamics, EV travel, and customer occupancy patterns in a concrete optimization framework. Extensive numerical results are presented to illustrate the efficacy of the proposed design. Specifically, we show that the proposed design can achieve significant saving in electricity cost, allow more flexibility in setting the tradeoff between cost and user comfort, and enable to reduce energy demand during peak hours. We also demonstrate the benefits of applying the proposed framework to a residential community compared to optimization of individual household separately.
260 citations
TL;DR: In this paper, the authors presented the rationale of China's two-phase EVSS and estimated their impacts on EV market penetration, with a focus on the ownership cost analysis of battery electric passenger vehicles (BEPV).
213 citations
TL;DR: In this article, the authors provide a review about measures and methods to achieve water supply systems efficiency, with and without investment in order to reduce costs and energy consumption, and explore the use of hydraulic simulation and optimisation strategies in water supply system, involving topics such as demand prediction, networks design, pumps operation, real-time operations and renewable energy production.
Abstract: The worldwide water supply represents a significant portion of the global energy consumption. This energy consumption, related to the collection, treatment and transportation of water, entails a large amount of costs. However, these costs are liable to be minimised with and/or without reduction on energy consumption. The purpose of this paper is to provide a review about measures and methods to achieve water supply systems efficiency. The paper summarises and compares previous investigations in order to provide the state-of-the-art to the reader. Measures with and without investment in order to reduce costs and energy consumption are presented. The paper also explores the use of hydraulic simulation and optimisation strategies in water supply systems, involving topics such as demand prediction, networks design, pumps operation, real-time operations and renewable energy production. Although the great advances in the area, there are unexplored (or poorly explored) methodologies that can be tested and maybe applied in a large number of water systems. There are also some important issues, mentioned in this paper, which must be considered in order to attend specific requirements of the water industries.
167 citations
TL;DR: In this paper, the relationship between quality improvement, reorder point, and lead time, as affected by backorder rate, in an imperfect production process is illustrated, and two lemmas are used to find optimal solutions for the basic and distribution free models.
130 citations
TL;DR: A novel two-stage design and the eco-IDsC (Energy Cost Optimization-IDC) algorithm is proposed to exploit the temporal diversity of electricity price and dynamically schedule workload to execute on IDC servers through an input queue.
Abstract: Cloud computing services are becoming integral part of people's daily life These services are supported by infrastructure known as Internet data center (IDC) As demand for cloud computing services soars, energy consumed by IDCs is skyrocketing Both academia and industry have paid great attention to energy management of IDCs This paper studies an important energy management problem-how to minimize energy cost for IDCs in deregulated electricity markets We propose a novel two-stage design and the eco-IDC (Energy Cost Optimization-IDC) algorithm to exploit the temporal diversity of electricity price and dynamically schedule workload to execute on IDC servers through an input queue Extensive evaluation experiments are performed using real-life electricity price and workload traces at an enterprise production data center The evaluation results demonstrate that the proposed approach significantly reduces energy cost for IDCs, guarantees a service delay bound, and alleviates workload drop if the service delay bound is sufficiently large
120 citations
TL;DR: In this article, the authors presented a comprehensive model of a small-scale integrated energy based district heating and cooling (DHC) system located in a residential area of hot-summer and cold-winter zone, which makes joint use of wind energy, solar energy, natural gas and electric energy.
93 citations
TL;DR: A distributed algorithm is proposed for the two systems to gradually and simultaneously reduce their costs from the non-cooperative benchmark to the Pareto optimum and this distributed algorithm also has proportional fair cost reduction by reducing each system's cost proportionally over iterations.
Abstract: Powered by renewable energy sources, cellular communication systems usually have different wireless traffic loads and available resources over time. To match their traffics, it is beneficial for two neighboring systems to cooperate in resource sharing when one is excessive in one resource (e.g., spectrum), while the other is sufficient in another (e.g., energy). In this paper, we propose a joint energy and spectrum cooperation scheme between different cellular systems to reduce their operational costs. When the two systems are fully cooperative in nature (e.g., belonging to the same entity), we formulate the cooperation problem as a convex optimization problem to minimize their weighted sum cost and obtain the optimal solution in closed form. We also study another partially cooperative scenario where the two systems have their own interests. We show that the two systems seek for partial cooperation as long as they find inter-system complementarity between the energy and spectrum resources. Under the partial cooperation conditions, we propose a distributed algorithm for the two systems to gradually and simultaneously reduce their costs from the non-cooperative benchmark to the Pareto optimum. This distributed algorithm also has proportional fair cost reduction by reducing each system's cost proportionally over iterations. Finally, we provide numerical results to validate the convergence of the distributed algorithm to the Pareto optimality and compare the centralized and distributed cost reduction approaches for fully and partially cooperative scenarios.
86 citations
TL;DR: In this paper, the authors focus on the techno-economic analysis of key technologies for energy recovery and re-use, namely: Organic Rankine Cycle (ORC), boiler feed water heating, heat pumping and absorption refrigeration in the context of process integration.
82 citations
TL;DR: In this article, the authors present an explicit treatment of the key uncertainties involved in learning rates' analyses of marine energy innovation (wave and tidal stream) -technology fields attracting considerable interest, but whose commercial prospects depends on substantial learning and cost reduction.
62 citations
TL;DR: Simulated annealing, a well-known stochastic search algorithm, is used to solve two cost minimization problems to address the capacity planning in an IaaS Cloud and shows that the optimal solutions are found within reasonable time.
Abstract: From an enterprise perspective, one key motivation to transform the traditional IT management into Cloud is the cost reduction of the hosted services. In an Infrastructure-as-a-Service (IaaS) Cloud, virtual machine (VM) instances share the physical machines (PMs) in the provider's data center. With large number of PMs, providers can maintain low cost of service downtime at the expense of higher infrastructure and other operational costs (e.g., power consumption and cooling costs). Hence, determining the optimal PM capacity requirements that minimize the overall cost is of interest. In this paper, we show how a cost analysis and optimization framework can be developed using stochastic availability and performance models of an IaaS Cloud. Specifically, we study two cost minimization problems to address the capacity planning in an IaaS Cloud: (1) what is the optimal number of PMs that minimizes the total cost of ownership for a given downtime requirement set by service level agreements? and, (2) is it more economical to use cheaper but less reliable PMs or to use costlier but more reliable PMs for insuring the same availability characteristics? We use simulated annealing, a well-known stochastic search algorithm, to solve these optimization problems. Results from our analysis show that the optimal solutions are found within reasonable time.
TL;DR: In this article, a case study for a new heliostat design is presented, which aims to provide directions and identify opportunities for cost reduction by examining trends at both a system level and at an individual collector level.
TL;DR: In this article, a market-based strategy is proposed for joint decisions on price, delivery time, service level, and supplier selection or investment, where the profit is maximized as the objective.
TL;DR: In this article, the authors proposed a cost-based droop scheme, whose objective is to reduce a generation cost function realised with various DG operating characteristics taken into consideration, where desired, proportional power sharing based on the DG kVA ratings can also be included, whose disadvantage is a slightly higher generation cost, which is still lower than that produced by the traditional droop schemes.
Abstract: In an autonomous microgrid where centralised management and communication links are not viable, droop control has been the preferred scheme for distributed generators (DGs). At present, although many droop variations have surfaced, they mainly focus on achieving proportional power sharing based on the DG kilovolts ampere (kVA) ratings. Other factors like generation costs, efficiencies and emission penalties at different load demands have not been considered. This omission might not be appropriate if different types of DGs are present in the microgrids. As an alternative, this study proposes a cost-based droop scheme, whose objective is to reduce a generation cost function realised with various DG operating characteristics taken into consideration. Where desired, proportional power sharing based on the DG kVA ratings can also be included, whose disadvantage is a slightly higher generation cost, which is still lower than that produced by the traditional droop schemes. The proposed droop scheme therefore retains all advantages of the traditional droop schemes, whereas at the same time, keeps its generation cost low. These findings have been validated in experiments.
TL;DR: In this article, the optimization of envelope design parameters by the use of integrated energy simulation and multi-dimensional numerical optimization techniques is proposed to minimize the life cycle costs for building materials and operational energy consumption of a reference commercial office building model.
Abstract: The minimization of life cycle costs for building materials and operational energy consumption of a reference commercial office building model is achieved through the optimization of envelope design parameters by the use of integrated energy simulation and multi-dimensional numerical optimization techniques. The whole-building energy simulation program EnergyPlus v6.0 is coupled with GenOpt v3.0 generic optimization tool to automatically compute the optimal values of thermal insulation thicknesses for external walls and roofs in addition to glazing unit types for vertical fenestration. A life cycle cost (LCC) model is implemented within the GenOpt program for the objective function evaluation using simulation outputs pertaining to energy consumption and associated utility costs. A stochastic population-based and multi-dimensional optimization technique of Particle Swarm Optimization (PSO) is utilized for searching the parameter space. This algorithm can result in a 36.2% reduction in the computational effort to converge to the global minimum point with a very high degree of accuracy compared to the full enumeration technique. The results indicate that the annual total site energy consumption of the optimized building model is reduced by 33.3% with respect to the initial baseline case. The optimized envelope parameters can yield 28.7% life cycle cost reduction over a 25 years life span with a simple pay-back period of 4.2 years.
TL;DR: A stochastic energy procurement model for large consumer with multiple options including distributed generations, bilateral contracts and pool market purchase considering DRP Pool market price uncertainty is modeled based on scenario distribution curve approach such as normal distribution curve as discussed by the authors.
Abstract: The consumers try to obtain their electricity demand at minimum cost from different resources in restructured electricity markets Hence more attention have been made on demand response programs (DRP) which aims to electricity price reduction, transmission lines congestion solution, security intensification and improvement of market liquidity and customer load benefit This paper develops a stochastic energy procurement model for large consumer with multiple options including distributed generations, bilateral contracts and pool market purchase considering DRP Pool market price uncertainty is modeled based on scenario distribution curve approach such as normal distribution curve The curve is divided into several areas, each area identified as a scenario, and the problem is solved using stochastic programming Also, this paper is focused to study the effect of DRP on total expected procurement cost has been discussed in all scenarios Actually a new stochastic framework using demand response program is presented for large consumer expected procurement cost reduction
TL;DR: In this article, the authors evaluate the economic performance of a substitute/synthetic natural gas (SNG) and power cogeneration technology with CO2 capture, and the role of technology localization and efficiency upgrade in cost reduction is investigated.
TL;DR: A risk-constrained stochastic programming decision framework is proposed for achieving the optimal tradeoff between operation risk and expected energy cost for Internet data center (IDC) operators in deregulated electricity markets according to the risk preferences of IDC operators.
Abstract: In this paper, we study the problem of achieving the optimal tradeoff between operation risk and expected energy cost for Internet data center (IDC) operators in deregulated electricity markets according to the risk preferences of IDC operators. To achieve the target above, we propose a risk-constrained stochastic programming decision framework. Then, we formulate a risk-constrained expected energy cost minimization problem with the uncertainties in spot price and workload. To solve the formulated problem, we use a decomposition-based cutting plane algorithm. Finally, extensive evaluations based on real-life data show the effectiveness of the proposed decision framework.
TL;DR: In this paper, a survey of hitherto concepts for cost reduction of heliostats is given, which might serve as a base for the development of low cost heliastats that are needed to meet the current challenging cost objectives.
Abstract: A survey of hitherto concepts for cost reduction of heliostats is given. The survey might serve as a base for the development of low cost heliostats that are needed to meet the current challenging cost objectives. The concepts are related to the main heliostat subfunctions and to basic approaches for cost reduction found so far. Based on the main advantages and drawbacks of every concept, the most promising ones are indicated.
TL;DR: In this paper, the authors compare the current German feed-in tariffs (FIT) with several alternative support mechanisms for renewable electricity, and show that the greatest cost reduction can be achieved under a technology-specific quantity-based system with a decrease in cumulated FIT differential costs of €68 billion and of €416 billion in total energy system costs between 2013 and 2030 compared to the current system.
TL;DR: A set of bidding strategies under several service-level agreement (SLA) constraints is proposed to minimize the monetary cost and volatility of resource provisioning and is able to obtain an optimal randomized bidding strategy through linear programming.
Abstract: With the recent introduction of Spot Instances in the Amazon Elastic Compute Cloud (EC2), users can bid for resources and, thus, control the balance of reliability versus monetary costs. Mechanisms and tools that deal with the cost-reliability tradeoffs under this scheme are of great value for users seeking to reduce their costs while maintaining high reliability. In this paper, we propose a set of bidding strategies under several service-level agreement (SLA) constraints. In particular, we aim to minimize the monetary cost and volatility of resource provisioning. Essentially, to derive an optimal bidding strategy, we formulate this problem as a Constrained Markov Decision Process (CMDP). Based on this model, we are able to obtain an optimal randomized bidding strategy through linear programming. Using real Instance price traces and workload models, we compare several adaptive checkpointing schemes in terms of monetary costs and job completion time. We evaluate our model and demonstrate how users should bid optimally on Spot Instances to reach different objectives with desired levels of confidence.
01 Nov 2014
TL;DR: This paper investigates how the distance-dependent renewable energy generation correlation between the two microgrids affects the energy cost saving, and proposes two online algorithms of low complexity for the real-time energy management of cooperative microgrid energy cooperation.
Abstract: The reliable operation of microgrids is hindered by the integration of intermittent renewable energy generations. To overcome this problem, in this paper we study the realtime energy management for two cooperative microgrids with integrated renewable energy generators. We first assume that renewable energy generations and loads of microgrids can be perfectly predicted or are known a priori. In this case, we solve the off-line optimization problem and propose a distributed algorithm for the optimal off-line energy management solution. Next, by defining energy cost saving as the total energy cost reduction with proposed microgrid energy cooperation over the traditional case of no cooperation, we investigate how the distance-dependent renewable energy generation correlation between the two microgrids affects the energy cost saving. Inspired by the observed energy cost saving versus distance behavior, we further propose two online algorithms of low complexity for the real-time energy management of cooperative microgrids. Last, we evaluate the performances of our proposed algorithms via simulations based on the Tucson power system data.
TL;DR: An integrated two-phase methodology to cope with the closed-loop supply chain design and planning problem is developed and such forecasting approaches can be applied to real-case CLSC problems in order to achieve more reliable design and plans of the network.
Abstract: Interests in Closed-Loop Supply Chain CLSC issues are growing day by day within the academia, companies, and customers. Many papers discuss profitability or cost reduction impacts of remanufacturing, but a very important point is almost missing. Indeed, there is no guarantee about the amounts of return products even if we know a lot about demands of first products. This uncertainty is due to reasons such as companies' capabilities in collecting End-of-Life EOL products, customers' interests in returning and current incentives, and other independent collectors. The aim of this paper is to deal with the important gap of the uncertainties of return products. Therefore, we discuss the forecasting method of return products which have their own open-loop supply chain. We develop an integrated two-phase methodology to cope with the closed-loop supply chain design and planning problem. In the first phase, an Adaptive Network Based Fuzzy Inference System ANFIS is presented to handle the uncertainties of the amounts of return product and to determine the forecasted return rates. In the second phase, and based on the results of the first one, the proposed multi-echelon, multi-product, multi-period, closed-loop supply chain network is optimized. The second-phase optimization is undertaken based on using general exact solvers in order to achieve the global optimum. Finally, the performance of the proposed forecasting method is evaluated in 25 periods using a numerical example, which contains a pattern in the returning of products. The results reveal acceptable performance of the proposed two-phase optimization method. Based on them, such forecasting approaches can be applied to real-case CLSC problems in order to achieve more reliable design and planning of the network
TL;DR: In this article, a single-stage hybrid production system with safety stocks and safety lead times that minimize inventory and backorder costs is presented. But the authors focus on a simplified M/M/1 system with exponentially distributed customer required lead time.
Abstract: This article models a single-stage hybrid production system, which can be regarded as a Make To Order (MTO) production system with safety stocks or a Make To Stock (MTS) production system with advance demand information. In an environment with multiple products and variable customer due dates, optimality conditions for safety stocks (base stocks) and safety lead times (work-ahead window) that minimize inventory and backorder costs are derived. For a simplified M/M/1 system with exponentially distributed customer required lead time, an explicit comparison between MTO and MTS is conducted. A pure MTO policy gets relatively more favorable to a pure MTS policy if inventory holding costs increase, backorder costs decrease, the mean customer required lead time increases, or the processing rate increases. In a numerical study, the influence of variance, the behavior of optimal parameters, and the cost reduction potential of this hybrid policy are shown.
04 Feb 2014
TL;DR: In this article, the benefits of cooperation compared to continuing with national renewable energy support after 2020 were analyzed, and the authors found that the cost reduction achieved by cooperation is quite robust with regard to assumptions about interconnector extensions and investment cost developments of renewable energy technologies.
Abstract: The availability of renewable energies differs significantly across European regions. Consequently, European cooperation in the deployment of renewable energy potentially yields substantial efficiency gains. However, for achieving the 2020 renewable energy targets, most countries purely rely on domestic production. In this paper, we analyze the benefits of cooperation compared to continuing with national renewable energy support after 2020. We use an optimization model of the European electricity system and find that compared to a 2030 CO2-only target (−40 % compared to 1990), electricity system costs increase by 5 to 7 % when a European-wide renewable energy target for electricity generation (of 55 %) is additionally implemented. However, these additional costs are 41 to 45 % lower than the additional costs which would arise if the renewable energy target was reached through national support schemes (without cooperation). Furthermore, the cost reduction achieved by cooperation is quite robust with regard to assumptions about interconnector extensions and investment cost developments of renewable energy technologies. In practice, however, administrative issues and questions concerning the fair sharing of costs and benefits between the Member States represent major obstacles that need to be tackled in order to reach renewable energy targets at the lowest costs possible.
TL;DR: To assess potential cost reduction of laparoscopic operations in the field of general surgery, hospital records, invoice lists, and operative notes between January 2012 and December 2013, were retrospectively reviewed and data was collected on Laparoscopic procedures, instrument failures, and replacement needs.
Abstract: Cost-effectiveness in health care management is critical. The situation in debt-stricken Greece is further aggravated by the financial crisis and constant National Health System expense cut-downs. In an effort to minimize the cost of laparoscopy, our department introduced reusable laparoscopic instruments in December 2011. The aim of this study was to assess potential cost reduction of laparoscopic operations in the field of general surgery. Hospital records, invoice lists, and operative notes between January 2012 and December 2013, were retrospectively reviewed and data were collected on laparoscopic procedures, instrument failures, and replacement needs. Initial acquisition cost of 5 basic instrument sets was €21,422. Over the following 24 months, they were used in 623 operations, with a total maintenance cost of €11,487. Based on an average retail price of €490 per set, projected cost with disposable instruments would amount to €305,270, creating savings of €272,361 over the two-year period under study. Despite the seemingly high purchase price, each set amortized its acquisition cost after only 9 procedures and instrument cost depreciated to less than €55 per case. Disposable instruments cost 9 times more than reusable ones, and their high price would almost equal the total hospital reimbursement by social security funds for many common laparoscopic procedures.
TL;DR: A novel approach to enable electrical energy buffering in batteries to predictively minimize IDC electricity costs in smart grid is proposed based on a discrete state-space model with states of battery power level and cost.
Abstract: More and more cloud computing services are handled by different Internet operators in distributed Internet data centers (IDCs), which incurs massive electricity costs. Today, the power usage of data centers contributes to more than 1.5% market share of electricity consumption across the United States. Minimization of these costs benefits cloud computing operators, and attracts increasing attentions from many research groups and industrial sectors. Along with the deployment of smart grid, the electrical real-time pricing policy promotes power consumers to adaptively schedule their electricity utilization for lower operational costs. This paper proposes a novel approach to enable electrical energy buffering in batteries to predictively minimize IDC electricity costs in smart grid. Batteries are charged when electricity price is low and discharged to power servers when electricity price is high. A power management controller is used per battery to arbitrate the charging and discharging actions of the battery. The controller is designed as a MPC-based (model predictive control) controller. To this end, an MPC power minimization problem is formulated based on a discrete state-space model with states of battery power level and cost. Extensive simulation results demonstrate the effectiveness of our approach based on real-life electricity prices in smart grid.
TL;DR: The results show that integrated model with milk-run delivery can reduce the total cost and realise the JIT production and procurement philosophy which emphasises small lot-size production and delivery.
Abstract: In a manufacturing system, a just-in-time (JIT) procurement and supply system is important for reducing cost and responding to customer’s requirement quickly. Successful implementation of a JIT system needs supplier/manufacturer cooperation in small lot-size delivery and inbound logistics cost reduction. In this study, an integrated optimal model of inventory lot-sizing vehicle routing of multisupplier single-manufacturer with milk-run JIT delivery is established. A novel method for computing transportation cost is proposed. Because the integrated model is a NP-hard problem, a meta-heuristic algorithm of ant colony optimisation is developed for solving the model. Numerical examples are used to demonstrate and test the effectiveness of the model and the algorithm. The results show that integrated model with milk-run delivery can reduce the total cost and realise the JIT production and procurement philosophy which emphasises small lot-size production and delivery. The results highlight the importance of coo...
Journal Article•
TL;DR: The PVSYST6.0.7 simulation results shows that the power generation costs for the grid connected solar powered system is less when compared to standalone solar power system in Benin City, Nigeria.
Abstract: Improved Quality of Service and cost reduction are important issues affecting the telecommunication industry. Companies such as Airtel, Glo etc believe that the solar powered cellular base stations are capable of transforming the Nigerian communication industry due to their low cost, reliability, and environmental friendliness. Currently, there are several research efforts directed on the use of solar power in the Nigerian telecommunication industry. In this paper, the importance of solar energy as a renewable energy source for cellular base stations is analyzed. Also, simulation software PVSYST6.0.7 is used to obtain an estimate of the cost of generation of solar power for cellular base stations. The simulations were carried out for the Grid-Connected and the Stand-Alone solar power systems by using Benin City, Nigeria as a case study. The PVSYST6.0.7 simulation results shows that the power generation costs for the grid connected solar powered system is less when compared to standalone solar powered system in Benin City, Nigeria