Capacity Expansion Costs Research Articles

Determination of ATC Using DCPTDFs Based Approach in Deregulated Market

Deregulated electricity industry aims to create a competition in market to trade electricity, which gives new opportunities to deal with technical challenges of market participants and power system researchers. The benefits are efficient capacity expansion planning, cheaper electricity, cost minimization, and better services. For better transmission services support in electricity industry and full utilization of transmission assets, Available Transfer Capability (ATC)must be calculated. Available transfer capability (ATC) of transmissionnetwork is the extra amount of power transfer that can betransmitted between the buyer bus and seller bus without any loss ofsecurity. For proper operations of system under the varioustransactions, ATC calculations must be fast andaccurate enough. In this paper by using DCPTDF scheme the ATC is calculatedwith and without line contingency caseand then the corresponding values of ATC are compared. The multiple line contingencies and their effecton the value of ATC are shown. The above said methodology is implemented on IEEE 24-bus test system.

Risk Management and Real Options with Operating Hedging Contracts

We analyze the optimal capacity investment (or operating leverage) and financial leverage policies with irreversible investment in the presence of financial distress and capacity expansion costs. In a loan market equilibrium with endogenous default, financial and operating leverage may either be substitutes or complements depending on the magnitude of expansion costs and whether the marginal tax rate exceeds marginal distress costs. Consequently, the relation of financial leverage and cash-flow uncertainty can be complex, consistent with empirical evidence. We generate novel predictions on the effects of financial and technological parameters on the cross-sectional variation in financial and operating leverage, and book-to-market.

A Feasibility Analysis Methodology for Decentralized Wastewater Systems – Energy‐Efficiency and Cost

Centralized wastewater treatment, widely practiced in developed areas, involves transporting wastewater from large urban areas to a large capacity plant using a single network of sewers, whereas decentralization is the concept of wastewater collection, treatment and reuse at or near its point of generation. Smaller decentralized plants can achieve extensive reclamation and wastewater management with energy-efficient reclaimed water pumping, modularized expansion and lower capital investment. We devised a methodology to preliminarily assess these alternatives using local constraints and conducted a feasibility analysis for each option. It addressed various scenarios using the pump-back energy consumption, sewer and treatment plant construction and capacity expansion cost. We demonstrated this methodology by applying it to the Hollywood vicinity (California). In this study, the decentralized configuration was more economical and energy-efficient than the centralized system. The pump-back energy consumption was about 50% of the aeration energy consumption for the centralized option.

Renewable resources portfolio optimization in the presence of demand response

Demand response is viewed as a practical and relatively low-cost solution to increasing penetration of intermittent renewable generation in bulk electric power systems. This paper examines the question of what is the optimal installed capacity allocation of renewable resources in conjunction with demand response. We introduce an integrated model for total annual system cost that can be used to determine a cost-minimizing allocation of renewable asset investments. The model includes production, uncertainty, emission, capacity expansion and mothballing costs, as well as wind variability and demand elasticity to determine the hourly cost of electricity delivery. The model is applied to a 2024 planning case for British Columbia, Canada. Results show that cost is minimized at about 30% wind generation. We find that the optimal amount of renewable resource is as sensitive to installation cost as it is to a carbon tax. But we find the inter-hourly demand response magnitude is much less helpful in promoting additional renewables than intra-hourly demand elasticity.

Development of an integrated optimization method for analyzing effect of energy conversion efficiency under uncertainty – A case study of Bayingolin Mongol Autonomous Prefecture, China

In this study, a superiority–inferiority full-infinite mixed-integer programming (SFMP) method is developed for analyzing the effect of energy conversion efficiency under uncertainty. SFMP can effectively tackle uncertainties expressed as fuzzy sets, crisp intervals and functional intervals, it also can directly reflect relationships among multiple fuzzy sets through varying superiority and inferiority degrees with a high computational efficiency. Then the developed SFMP is applied to a real case of planning energy system for Bayingolin Mongol Autonomous Prefecture, where multiple scenarios related to different energy-conversion efficiency are concerned. Results for energy processing, energy conversion, capacity expansion, pollutant emission and system cost have been generated. It is proved that SFMP is an effective approach to deal with the uncertainties in energy systems with interactive and uncertain characteristics. A variety of uncertainties existed in energy conversion processes and impact factors could affect the modeling result. Results show that improvement of energy-conversion efficiency can effectively facilitate reducing energy resources consumption, optimizing energy generation pattern, decreasing capacity expansion, as well as mitigating pollutant emissions. Results also reveal that, for the study area, electric power has a highest energy saving potential among heating, oil processing, coal washing and refining. Results can help decision makers to generate desired alternatives that can facilitate policy enactment of conversion efficiency improvement and adjustment of regional energy structure under uncertainty.

Granular robust mean-CVaR feedstock flow planning for waste-to-energy systems under integrated uncertainty.

In the context of robust optimization with information granules for distributional parameters, this paper investigates a two-stage waste-to-energy feedstock flow planning problem with uncertain capacity expansion costs. The objective is to minimize the worst-case overall loss in a mean-risk criterion where the risk is measured by a conditional value-at-risk operator. As a salient feature, an integrated uncertainty is considered which consists of not only the uncertainty in distribution shapes of the uncertain variables, but also the manifold uncertainties of the mean parameters. To tackle the robust optimization under such integrated uncertainty, we first discuss a distributional robust two-stage feedstock flow planning model with precise mean parameters that handles the uncertainty in distribution shape, and the model can be equivalently transformed into a linear program (LP). Furthermore, the precise-mean-based robust model is extended into the case of multifaceted uncertainty for mean-parameters that are allowed to assume intervals, historical-data-based probabilistic estimates, and/or human-knowledge-centric fuzzy set estimates, under different circumstances. These multifaceted uncertain mean-parameters are uniformly represented by using information granules, and a granular robust optimization model is then developed which maximizes the robustness of the solution within a shortfall tolerance, and realizes a tradeoff between the solution conservativeness and robustness. It is showed that the granular robust model is equivalent to solving a series of LPs and can be efficiently handled by a nested binary search algorithm. Finally, the computational study illustrates the model performance, solution analysis, and underlines a much higher scalability of the developed robust model compared to the stochastic programming approach.

Analytical frameworks to incorporate demand response in long-term resource planning

Many utilities are obligated by state regulatory or legislative requirements to consider demand response (DR) as part of their resource planning process. There are several ways to incorporate DR into resource planning modeling and each has its advantages and disadvantages. We explore the current analytical frameworks for incorporating DR into long-term resource planning. We also consider whether current approaches accurately and realistically model DR resources in capacity expansion and production cost models and whether barriers exist to incorporating DR into resource planning models in a more robust fashion. We identify 10 specific recommendations for enhancing and expanding the current approaches.

Estimating the potential of controlled plug-in hybrid electric vehicle charging to reduce operational and capacity expansion costs for electric power systems with high wind penetration

Electric power systems with substantial wind capacity require additional flexibility to react to rapid changes in wind farm output and mismatches in the timing of generation and demand. Controlled variable-rate charging of plug-in electric vehicles allows demand to be rapidly modulated, providing an alternative to using fast-responding natural gas plants for balancing supply with demand and potentially reducing costs of operation and new plant construction. We investigate the cost savings from controlled charging of electric vehicles, the extent to which these benefits increase in high wind penetration scenarios, and the trade-off between establishing a controlled charging program vs. increasing the capacity of generators in the power system. We construct a mixed integer linear programming model for capacity expansion, plant dispatch, and plug-in hybrid electric vehicle (PHEV) charging based on the NYISO system. We find that controlled charging cuts the cost of integrating PHEVs in half. The magnitude of these savings is ∼5% to 15% higher in a system with 20% wind penetration compared to a system with no wind power, and the savings are 50–60% higher in a system that requires capacity expansion.

Wholesale price rebate vs. capacity expansion: The optimal strategy for seasonal products in a supply chain

We consider a supply chain in which one manufacturer sells a seasonal product to the end market through a retailer. Faced with uncertain market demand and limited capacity, the manufacturer can maximize its profits by adopting one of two strategies, namely, wholesale price rebate or capacity expansion. In the former, the manufacturer provides the retailer with a discount for accepting early delivery in an earlier period. In the latter, the production capacity of the manufacturer in the second period can be raised so that production is delayed until in the period close to the selling season to avoid holding costs. Our research shows that the best strategy for the manufacturer is determined by three driving forces: the unit cost of holding inventory for the manufacturer, the unit cost of holding inventory for the retailer, and the unit cost of capacity expansion. When the single period capacity is low, adopting the capacity expansion strategy dominates as both parties can improve their profits compared to the wholesale price rebate strategy. When the single period capacity is high, on the other hand, the equilibrium outcome is the wholesale price rebate strategy.

The storage capacity expansion and space leasing for container depots

Positioning empty containers is one of the most effective ways to solve the container imbalance problem and is affected by the capacity of depots. A shipping company will be more competitive if the depot capacity is large. This study provides a decision tool for planning the expansion of depot capacity. Mathematical models are utilized to minimize the total relevant costs that include the capacity expansion cost, storage space leasing cost, inventory holding cost, container leasing cost, and positioning cost. The problem is formulated as a mixed integer program. Then, we develop a heuristic algorithm that is based on Lagrangian relaxation. Computational experiments are conducted to evaluate the performance of the proposed algorithm.

A multi-objective stochastic programming approach for supply chain design considering risk

In this paper, we develop a multi-objective stochastic programming approach for supply chain design under uncertainty. Demands, supplies, processing, transportation, shortage and capacity expansion costs are all considered as the uncertain parameters. To develop a robust model, two additional objective functions are added into the traditional comprehensive supply chain design problem. So, our multi-objective model includes (i) the minimization of the sum of current investment costs and the expected future processing, transportation, shortage and capacity expansion costs, (ii) the minimization of the variance of the total cost and (iii) the minimization of the financial risk or the probability of not meeting a certain budget. The ideas of unreliable suppliers and capacity expansion, after the realization of uncertain parameters, are also incorporated into the model. Finally, we use the goal attainment technique to obtain the Pareto-optimal solutions that can be used for decision-making.

Research on Capacity Planning under Stochastic Production and Uncertain Demand

Abstract The main task of the capacity planning decision is to determine an optimal schedule to replace older machines, equipments or activity centers by newer ones. Traditionally, uncertain factors are seldom considered in capacity expansion because of an approximation view in medium- and long-term decision-making. However, in an uncertain market and stochastic production environment, the influences of stochastic factors should not be neglected entirely. To study the stochastic factors' influences on capacity planning decision, a stochastic capacity expansion model is built in this article. The model's objective is to minimize the production and capacity expansion cost. There are two kinds of stochastic factors to be considered: uncertain market demand and consumption of stochastic production capacity. For solving this model, the constraints of uncertain demand are transformed into equivalent deterministic constraints, using the stochastic two-stage model. The constraints of stochastic capacity are transformed into equivalent deterministic formulation, based on Chance Constrained Programming (CCP). Consequently, the stochastic capacity planning model is transformed into a deterministic one, which has a structure where the Product-Mix decision is nested in the capacity planning model. To solve this two-tier model, a heuristic algorithm that combines the Genetic Algorithm (GA) and the Primal-Dual algorithm of Nonlinear Programming, is produced. In the heuristic, GA is used to optimize the capacity decision variables and the Primal-Dual method is used to solve a Product-Mix decision problem with quadratic constraints. A numerical example is constructed for demonstrating the model and the algorithm. The results and analysis are provided.

Strategic level three-stage production distribution planning with capacity expansion

In this paper, we address a strategic planning problem for a three-stage production-distribution network. The problem under consideration is a single-item, multi-supplier, multi-producer, and multi-distributor production-distribution network with deterministic demand. The objective is to minimize the costs associated with production, transportation, and inventory as well as capacity expansion costs over a given time horizon. The limitations are the production capacities of the suppliers and producers, and transportation capacities of the corresponding transportation network. On the other hand, all capacities may be increased at a fixed cost. The problem is formulated as a 0-1 mixed integer programming model. Since the problem is intractable for real life cases efficient relaxation-based heuristics are considered to obtain a good feasible solution.

Optimization of Internet Protocol network design and routing

AbstractWe consider network design and routing for Internet Protocol (IP) traffic. The design problem concerns capacity dimensioning of communication links, where the design cost consists of fixed charges and linear capacity expansion costs. The optimization problem also concerns determining the amount of traffic demand to be carried by the network and the metric used by a shortest path routing protocol. We present a novel linear mixed‐integer mathematical formulation and two heuristic solution procedures. The first heuristic uses mixed‐integer programming to generate a sequence of routing solutions. The second solution approach is a simulated annealing meta heuristic. Computational experiments for synthesized and real‐life networks show that high‐quality solutions can be obtained by both approaches. © 2003 Wiley Periodicals, Inc.

Optimal machine capacity expansions with nested limitations under stochastic demand

AbstractThis paper studies capacity expansions for a production facility that faces uncertain customer demand for a single product family. The capacity of the facility is modeled in three tiers, as follows. The first tier consists of a set of upper bounds on production that correspond to different resource types (e.g., machine types, categories of manpower, etc.). These upper bounds are augmented in increments of fixed size (e.g., by purchasing machines of standard types). There is a second‐tier resource that constrains the first‐tier bounds (e.g., clean room floor space). The third‐tier resource bounds the availability of the second‐tier resource (e.g., the total floor space enclosed by the building, land, etc.). The second and third‐tier resources are expanded at various times in various amounts. The cost of capacity expansion at each tier has both fixed and proportional elements. The lost sales cost is used as a measure for the level of customer service. The paper presents a polynomial time algorithm (FIFEX) to minimize the total cost by computing optimal expansion times and amounts for all three types of capacity jointly. It accommodates positive lead times for each type. Demand is assumed to be nondecreasing in a “weak” sense. © 2003 Wiley Periodicals, Inc. Naval Research Logistics, 2004.

Power sector development in India with CO2 emission targets: Effects of regional grid integration and the role of clean technologies

The power sector in India at present comprises of five separate regional electricity grids having practically no integrated operation in between them. This study analyses the utility planning, environmental and economical effects of integrated power sector development at the national level in which the regional electric grids are developed and operated as one integrated system. It also examines the effects of selected CO2 emission reduction targets in the power sector and the role of renewable power generation technologies in India. The study shows that the integrated development and operation of the power system at the national level would reduce the total cost including fuel cost by 4912 million $, total capacity addition by 2784 MW, while the emission of CO2, SO2 and NOx would be reduced by 231.6 (1.9%), 0.8 (0.9%), 0.4 (1.2%) million tons, respectively, during the planning horizon. Furthermore, the study shows that the expected unserved energy, one of the indices of generation system reliability, would decrease to 26 GWh under integrated national power system from 5158 GWh. As different levels of CO2 emission reduction targets were imposed, there is a switching of generation from conventional coal plants to gas fired plants, clean coal technologies and nuclear based plants. As a result the capacity expansion cost has increased. It was found that wind power plant is most attractive and economical in the Indian perspective among the renewable options considered (Solar, wind and biomass). Copyright © 2003 John Wiley & Sons, Ltd.

A Heuristic Approach to Satellite Link Capacity Planning Applied in Mobile Networks

The efficient heuristic algorithm for sizing of N satellite capacitytypes (on LES side of/ink) in mobile networks during exploitationis being developed, minimizing the total expansioncost. Using the network flow approach and the extreme flowthe01y many expansion solutions (sub-problems) are eliminatedfrom further calculation because they consist at least ofone flow that cannot be a part of optimal expansion sequence.At first, the heuristic algorithm is compared with algorithmbased on exact approach. In all numerical test-examples thebest possible result is achieved. After that we developed andtested some algorithm options, using various limitations for capacitystate values. It is obvious that all heuristic options areve1y effective and for some cases they are capable to find thebest possible result but with significant savings. It means thatour heuristic approach can be successfully applied to short-term or medium-term satellite network planning with finitenumber of discrete time periods. Only if we apply adequate heuristicsolution can we ensure both significant improvement ofQoS (Quality of Setvice) and minimal capacity expansion cost.

L-SHAPED DECOMPOSITION METHOD FOR MULTI-STAGE STOCHASTIC CONCENTRATOR LOCATION PROBLEM

A stochastic version of a concentrator location problem is dealt with in which traffic demand at each terminal location is uncertain. The concentrator location problem is defined as to determine the following: (i) the numbers and locations of concentators that are to be open, and (ii) the allocation of terminals to concentrator sites. The problem is formulated as a stochastic multi-stage integer linear program, with first stage binary variables concerning network design and continuous recourse variables concerning expansion of capacity. Given a first stage decision, the series of realization of traffic demand may possibly imply a violation of the capacity constraint of the concentrator. Therefore from the second stage to the last stage, recourse action is taken to correct the violation. The objective function minimizes the cost of connecting terminals and the cost of opening concentrators and the expected recourse cost of capacity expansion. We propose a new algorithm which combines an L-shaped method and a branch-and-bound method. Under some assumptions it decomposes the problem into a set of problems as many as the number of stages in parallel. Finally we demonstrate the computational efficiency of our algorithm for the multi-stage model.

Design and Configuration of Reconfigurable ATM Networks with Unreliable Links

This paper considers a problem of configuring both physical backbone and logical virtual path (VP) networks in a reconfigurable asynchronous transfer mode (ATM) network where links are subject to failures. The objective is to determine jointly the VP assignment, the capacity assignment of physical links and the bandwidth allocation of VPs, and the routing assignment of traffic demand at least cost. The network cost includes backbone link capacity expansion cost and penalty cost for not satisfying the maximum throughput of the traffic due to link failures or insufficient link capacities. The problem is formulated as a zero-one non-linear mixed integer programming problem, for which an effective solution procedure is developed by using a Lagrangean relaxation technique for finding a lower bound and a heuristic method exploited for improving the upper bound of any intermediate solution. The solution procedure is tested for its effectiveness with various numerical examples.

JOINT CONFIGURATION OF BACKBONE AND LOGICAL NETWORKS ON A RECONFIGURABLE PACKET-SWITCHED NETWORK WITH UNRELIABLE LINKS

This paper considers a problem of configuring both backbone and logical networks in a reconfigurable packet-switched network where links are subject to failures. The objective is to design feasible backbone and logical networks at least cost where the network cost includes backbone link capacity expansion cost and average packet delay penalty cost due to link failures. The problem is formulated as a zero-one non-linear mixed integer programming problem, for which an effective solution procedure is developed by using a Lagrangean relaxation technique for finding a lower bound and a heuristic method is exploited for improving the upper bound of an intermediate solution. The solution procedure is tested for its effectiveness with various numerical examples.