Total Net Present Value Research Articles

О важности стратегического подхода при ценообразовании на рынках сетевых благ

The paper is dedicated to dynamic pricing strategy in duopoly markets for network goods. Duopoly members are regarded as strategy-oriented agents that are willing to sacrifice their current income for the increase in the total outcome (net present value). The results of the study prove that in duopoly markets for network goods there exist equilibrium dynamic pricing strategies that have the following features: a) both suppliers get the largest possible total net present value; b) if one of the suppliers pursues the equilibrium strategy while the other one does not, the later makes losses compared to the income provided by the equilibrium strategy. The strategy of moderately high comparable prices is considered to be the most advantageous one since it allows to reach the critical mass of users rather quickly as well as to bring similar gains to both players. If duopoly members pursue pricing strategies that are different from each other, their current results also differ. But when the loser at an intermediate stage tries to adopt the pricing strategy of the competitor, this can lead him to new losses. Such consequences prove the importance of a strategic approach to pricing in markets for network goods.

Increasing Benefits in High PV Penetration Distribution System by Using Battery Enegy Storage and Capacitor Placement Based on Salp Swarm Algorithm

This work aims to maximize the benefit of the low-voltage (LV) level distribution system with high photovoltaic (PV) penetration by using an optimal installation of a battery energy storage system (BESS) and capacitor. The 41-bus practical distribution system located in Thailand was focused on. The comprehensive objective function regarding the focused system was proposed. The Salp Swarm and Genetic Algorithms were applied to solve the optimization problem. The total net present value (NPV) of utility was performed as a beneficial indicator, and it was determined by the overall costs and benefits of BESS installation and capacitor placement. A comparison of total NPV in the cases of centralized BESS installation, BESS installation with LV capacitor placement, and decentralized BESS installation was indicated. The results showed that all cases of BESS installation could increasingly flatten the load on the transformer; meanwhile, the voltage profile of the system was significantly improved. Optimal installation of centralized BESS simultaneously with LV capacitor placement provides higher NPV than the case with only centralized BESS installation. In particular, the highest NPV was obtained in the case of installing decentralized BESS. The results can be utilized to maximize the benefits of the utility in the distribution system at a high PV penetration level.

OPTIMIZATION AND SIZING OF AN ISLAND MICROGRID BASED ON PHOTOVOLTAIC/FUEL CELL (PV/FC) IN KSA

The major goal of this research manuscript is to conduct economical and feasibility studies of an island photovoltaic/fuel cell (PV/FC) hybrid system. It has been designed to feed electricity to a remote area positioned in albaha zone in Kingdom of Saudi Arabia. To achieve this mission, all system devices are modelled and simulated via optimization mechanisms. An objective function is formulated relied on the total annual cost (TAC). An optimization mechanism, the Flower Pollination Algorithm (FPA) has been applied to obtain the optimum number of the photovoltaic (PV) panels, the electrolyzers; the H2 storage tanks the fuel cells (FCs) and the number of batteries for the proposed system with the least total net present value (TNPV) of the hybrid system. The loss of power supply probability (LPSP) has been utilized to improve the performance of the proposed system. The premier estimation fluctuation effect of the FC, the electrolyzer and the H2 storage tanks on the levelized cost of energy (LCOE) has been applied in the sensitivity test.

Efficiency of institutional spending and investment rules

ABSTRACTEndowment funds and similar institutions aim to generate a benefit stream of unlimited duration on the basis of an initially donated capital. Towards this purpose, responsible trustees need to design a spending policy as well as an investment policy. A combined spending and investment policy is said to be efficient if the total net present value of benefits that are paid according to the policy is equal to the initial capital, and inefficient if the total net present value is less than that. For several strategies, analytical expressions are given for the total net present value of benefits under the Black-Scholes assumptions. One of the strategies considered is the combination of a fixed-mix investment policy with a benefit policy that pays inflation-indexed benefits as long as ruin does not occur. This strategy is shown to be inefficient in many cases; the effective loss of capital can range from 5% to 15% under realistic parameter values. The inefficiency can be removed by adapting the investment policy and raising the benefits, without increasing the probability of ruin.

Oversaving in Long-Term Funds

Endowment funds and similar institutions aim to generate a benefit stream of unlimited duration on the basis of an initially donated capital. Towards this purpose, responsible trustees need to design a spending policy as well as an investment policy. A combined spending and investment policy is said to be efficient if the total net present value of benefits that are paid according to the policy is equal to the initial capital, and inefficient if the total net present value is less than that. For several strategies, analytical expressions are given for the total net present value of benefits under the Black-Scholes assumptions. One of the strategies considered is the combination of a fixed-mix investment policy with a benefit policy that pays inflation-indexed benefits as long as ruin does not occur. This strategy is shown to be inefficient in many cases; the effective loss of capital can range from 5% to 15% under realistic parameter values. The inefficiency can be removed by adapting the investment policy and raising the benefits, without increasing the probability of ruin.

PETROLEUM PRODUCTION SHARING CONTRACTS IN THE MIDDLE EAST: APPLICATION OF ECONOMIC EVALUATION AND DECISION-MAKING MODELING

In this Study, the evolution and history of petroleum contracts is discussed with a focus on the recent major shift witnessed from Joint Operating Agreements (JOA) and Concession contracts to Production Sharing Agreements (PSA) due to the increase in the complexity of operations and funding difficulties especially from the government’s side. In addition to that, the fiscal system of PSA’s is studied as a legal instrument to allocate risk between the parties, identify ownership of assets, commitments and operational control. This study aims to figure out the application of economic evaluation for PSA’s using Discounted Cash Flow Analysis (DCF) which is considered to be powerful in evaluating the investment performance by calculating Net Present Value (NPV) of cash flows and the Internal Rate of Return (IRR). The estimated production profile submitted in the PSA of interest was exaggerated. When a Monte-Carlo Simulation was run the riskier profile (P10) was the closest to the PSA production forecast. However, in such agreements usually the mid case scenario (P50) is taken into consideration. The NPV for the first 10 years in this project is around $0.45 Billion compared to the total NPV calculated using the economic model $1.26 Billion. However, the recent instability of the oil price in the last 10 years what causes this project in particular not to meet the target so far. The IRR calculated for the 10 years period is almost the hurdle rate 10% and this is again due to the unexpected NPV due to the oil price. To reduce the uncertainty, pilot projects needs to be conducted in the first year in smaller spacing to allow for the response to be detected faster and then develop a Field Development Plan (FDP).

Profit allocation in collaborative bioenergy and biofuel supply chains

In this paper, the profit allocation problem, which was overlooked in the collaborative bioenergy and biofuel supply chain literature, is considered. The optimization model for the strategic and tactical planning of a bioenergy and biofuel supply chain is modified and a number of cooperative game theory allocation methods are evaluated for a case study of collaboration among three potential bioconversion facilities in British Columbia, Canada. Moreover, a sensitivity analysis is conducted to evaluate the stability of the methods with changes in parameters. The results show that the collaboration of three bioconversion plants would generate the highest profit compared with other coalitions. Based on the results, the savings from collaboration could improve the total net present value by 5%. The results highlight that the profitability and stability of collaboration are closely related to bio-oil market prices and demand. The proportional allocation methods hardly generate stable (efficiency and individual rationality) results. Therefore, caution should be taken about the frequent use of proportional methods for costs/benefits allocation by the industry. Instead, the nucleolus and equal profit methods are recommended as alternative methods since they are always stable regardless of variations in model parameters, such as bioproduct demand and prices.

How does an industry manage the optimum cash flow within a smart production system with the carbon footprint and carbon emission under logistics framework?

The optimized cash flow may affect any smart production system to control a material requirement planning and to reduce the carbon footprint within the environment. An automation policy is utilized within a smart production system under a forward and a backward logistics system. Such logistic network generally consists of a well-structured transportation system, which may increase the carbon footprint. This study deals how the carbon footprint can be controlled by a smart production system and it obtains the net present value of products for the four sub-systems associated with the logistics system. To investigate this, four sub-systems as manufacturing, distribution, consumption, and remanufacturing are implemented. A solution methodology is designed with an integral transformation through the frequency domain. This smart logistics system can be used by an associated matrix through an input-output analysis based on the distribution center. An illustrative numerical experiment is conducted and the study reveals that the discounted sale in disposal subsection at the end of the logistic cycle gives high positive impact, where the efficiency is increased due to discarding defective products by the automation policy. Graphical studies on the effect of transportation time for the total net present value and the net present value for disposal items are compared. It is found that two-stage inspection process reveals less amount of defective items and less pollution. As the closed-loop supply chain management is considered and due to transportation, huge amount of carbon emissions are passing through the environment, this study gives the reduced amount of carbon and more perfect products by an optimum cash-flow within a smart production system under advanced logistics management.

Machine learning models to support reservoir production optimization

Traditionally, numerical simulators are used in combination with an optimization algorithm to determine optimum controls that maximize total oil production or net present value (NPV) over the life of the reservoir. These simulators are complex dynamic models that consider geological information, rock and fluid properties, as well as information about the completion of the wells. This complexity results in a high computational time and pose a challenge for the application of gradient-based optimization algorithms, since calculation of gradients of the objective function with respect to controls may demand several evaluations of the simulation model. This paper proposes the use of a machine learning model, based on artificial neural networks, to represent the non-linear dynamic behavior of the reservoir. The proposed approach was applied to data generated with a synthetic reservoir simulation model showing promising results.

Economics of Hydro-Kinetic Turbine for off-grid Application: A Case Study of Gumara River, Upper Blue Nile, Amhara, Ethiopia

This paper examines the economics of Hydro-kinetic (HK) turbines to investigate the possibility of usage in rural areas, where the grid is not available. In support of the different efforts endeavored by the Ethiopian Government to make electricity available to all, the project is called `light for all'. This study was carried out in the Gumara River, upper Blue Nile, and Amhara Region Ethiopia. The energy demand of a representative household family is selected for this study. We implemented the most commonly used domestic home appliances for the model with a total wattage of 2.28 kW at base load and 4.147 kW at peak load. The total daily power demand of the typical household in the area is 19.26 kWh/day. Then to determine costs of HK we used the Life Cycle Cost Analysis (LCCA) methodology. The initial capital cost, replacement cost, and operating and maintenance cost were calculated to determine the economics of HK turbine. Then, we applied the HOMER software system design of 5 kW HK turbine and 2.5 kW diesel generator to estimate the economic cost of HK. In our study, we used $20 per metric ton of carbon emission penalty. The IRR was estimated to be 17.4% and the payback period of 5.6 years with a present worth of $4826, and the total Net Present Value of $89,764. The estimated Levelized cost of energy was about $0.42. Our finding indicates that the HK technology is economically and technically viable energy option for small scale o-grid electrification. This work also alludes to risk analysis on Hydro-kinetic turbines.

The ageing workforce challenge: Investments in collaborative robots or contribution to pension schemes, from the multi-echelon perspective

In Europe, the number of people aged 65 or older is about to grow from 85 million today to more than 151 million in 2060, with a decrease of 20 million in the number of workers. To keep the public finances and pension funds sustainable, the retirement age of industrial workers in national pension schemes is rising. However, workers in some specific workplaces are not able to work until they have reached the increased retirement age. Often they are producing fewer products, in some workplaces also with lower quality, and they are causing substantial delays in the multi-echelon system mostly because of response latency. On the other hand, value-added, personalised products/services often demand skilled and experienced older workers, who need better ergonomic support. To solve this problem, companies have two options: (a) to develop new supplementary occupational pension schemes which would compensate the required increase in retirement age or (b) to invest in ergonomic improvements of the working places/processes, to provide a better working environment and reduce the response latency. In this paper, the trade-off between these two options is investigated, by introducing a model based on MRP theory for evaluating the Net Present Value (NPV) of activities and investments of a multi-echelon system. The numerical example related to the production of cars in Italy shows that even varying the cost of labour and the investment in collaborative robots, the model demonstrates that investments in ergonomics in case of ageing workforce increases the total NPV of the multi-echelon system.

Joint Dynamic Pricing and Inventory Control for Perishable Products Taking into Account Partial Backlogging and Inflation

A joint dynamic pricing and inventory model by considering the inflation and time value of money where the product has a limited lifetime is developed. The selling price depends on product lifetime and customer demand rate depends on the selling price and time. Also, the shortages are considered and partially backlogged. The main goal is to determine the optimal initial selling price, the economic order quantity, and the optimal replenishment period simultaneously in order to maximize the total net present value of profit. We represent that for given any number of replenishment the function of profit is concave. In order to find the optimal solution, the presented heuristic algorithm is applied. To represent the algorithm and procedure of solution, a numerical example is solved. The results show that applying the proposed dynamic pricing approach leads to a significant improvement in profit’s retailer. Finally, sensitivity analysis of key parameters model is performed and some managerial insights are presented.

A location-routing problem for the design of an asteroid mining supply chain network

This paper considers the design of a supply chain network for the delivery of asteroid-derived resources to customers in Earth orbit. The network features orbital nodes consisting of an asteroid source, an orbital propellant depot, arrival and departure parking orbits, and customer spacecraft. Edges between nodes represent transfer trajectories modelled as impulsive manoeuvres. Two classes of spacecraft vehicles are assigned to transport the product through various edges in the network. We determine the optimal location of nodes, and vehicle routes through the network (a location-routing problem) with the objective of maximizing either the total sellable mass delivered to customers, or the total net present value of the asteroid mining venture. The depot and parking orbit locations are to be selected from a discrete set of candidate locations with variable orbital radius and orientation. Vehicle routes are to be chosen from two candidate routes that ensure reusability over multiple mining trips. A mathematical formulation is presented to describe the objective function as a function of the candidate node locations and vehicle routes. We develop a set of constrained optimization problems to determine the optimal location and orientation of parking orbits, depot location, and vehicle routes though the network. Solution methods are developed using integer programming. A numerical example is presented to illustrate the method for the case of maximizing total sellable mass delivered from a near-Earth asteroid to a single customer in Geostationary Orbit. The results showed that the maximum sellable mass is achieved with a delivery route in which the mining spacecraft remains in a highly elliptical parking orbit before returning to the asteroid, while a small transport spacecraft retrieves the load of material and delivers it to an orbital propellant depot and customers. The optimal location of a propellant depot is found to be at the same orbital radius and orbital plane as the customers. This depot location and vehicle routing can deliver between 33.15 and 52.63% of the mass extracted from the asteroid in a single trip (depending on the trajectories to/from the target asteroid), with the ability to conduct numerous return trips to the asteroid.

Real‐Options Water Supply Planning: Multistage Scenario Trees for Adaptive and Flexible Capacity Expansion Under Probabilistic Climate Change Uncertainty

AbstractPlanning water supply infrastructure includes identifying interventions that cost‐effectively secure an acceptably reliable water supply. Climate change is a source of uncertainty for water supply developments as its impact on source yields is uncertain. Adaptability to changing future conditions is increasingly viewed as a valuable design principle of strategic water planning. Because present decisions impact a system's ability to adapt to future needs, flexibility in activating, delaying, and replacing engineering projects should be considered in least‐cost water supply intervention scheduling. This is a principle of Real Options Analysis, which this paper applies to least‐cost capacity expansion scheduling via multistage stochastic mathematical programming. We apply the proposed model to a real‐world utility with many investment decision stages using a generalized scenario tree construction algorithm to efficiently approximate the probabilistic uncertainty. To evaluate the implementation of Real Options Analysis, the use of two metrics is proposed: the value of the stochastic solution and the expected value of perfect information that quantify the value of adopting adaptive and flexible plans, respectively. An application to London's water system demonstrates the generalized approach. The investment decisions results are a mixture of long‐term and contingency schemes that are optimally chosen considering different futures. The value of the stochastic solution shows that by considering uncertainty, adaptive investment decisions avoid £100 million net present value (NPV) cost, 15% of the total NPV. The expected value of perfect information demonstrates that optimal delay and early decisions have £50 million NPV, 6% of total NPV. Sensitivity of results to the characteristics of the scenario tree and uncertainty set is assessed.

A flower pollination optimization algorithm for an off-grid PV-Fuel cell hybrid renewable system

This research work crucially deals with a techno-economic feasibility study for off-grid solar photovoltaic fuel cell (PV/FC) hybrid systems. The hybrid renewable energy system is investigated for feeding electric to remote areas and isolated urban regions in Egypt. To achieve this goal, all the system equipment are modeled, simulated and the area under study data is gathered. The objective function is formulated depending on the total annual cost (TAC). The Flower Pollination Algorithm (FPA), as an efficient recent metaheuristic optimization method, proposed to estimate the optimum number of both PV panels and the FC/electrolyzer/H2 storage tanks set mandatory where the least total net present value (TNPV) is reached.The loss of power supply probability (LPSP) is considered to enhance the performance of the proposed design. The effect of the variation of FC, electrolyzer, H2 storage tanks and the PV power system initial cost on the levelized cost of energy (LCOE) is presented through a comprehensive sensitivity analysis.Through Matlab™ program, the numerical simulation results obtained by the FPA algorithm have been compared to the corresponding outcomes while using the artificial bee colony (ABC) and the Particle Swarm Optimization (PSO) techniques. According to the simulation outcomes analysis, the FPA Algorithm has the less fulfillment time and good rendering between the other algorithms. In addition, the optimum system configuration is acquired using FPA with the optimal hybridization of 27 solar PV, 28 FCs, 58 electrolyzers and 37 H2 storage tanks for an LPSP and PEE of 1.52% and 4.68% respectively. The system TNPV is $3,244,897 with the LCOE of 0.334 $/kWh.

Integrating bilberry yields into regional long-term forest scenario analyses

ABSTRACTIn many areas, picking wild berries constitutes an important forest commodity offering food, recreational services, and revenues. Intensified forest management has negatively affected bilberry yields. In this study, regional-level scenario analyses were conducted to investigate the effects of four approaches to the inclusion of bilberry yields on the economics, management, and resulting forest structures in North Karelia. The current approach ignoring bilberries resulted in decreasing bilberry yields and increasing younger forests. When bilberries were valued at market price (1.72 € kg−1), bilberry yields still decreased despite the fact that forest management were slightly modified to favour bilberry. Adding non-declining sustainability constraints to bilberry yields resulted in a slightly lower total net present value of timber and bilberry since forest management favoured longer rotation lengths, which increased bilberry yields. Similar effects occurred when the bilberry price was tripled to include the implicit ecosystem benefits of bilberries. When forests were managed only for bilberries (timber zero-priced), bilberry yields started to increase immediately resulting in 1.5-fold difference in 50 years as compared to timber only scenario. In conclusion, managing forests for both timber and bilberry production has rather minor effects on economics, but it can have clear positive effect on bilberry yields.

Induced Technological Change in a Limited Foresight Optimization Model

The threat of global warming calls for a major transformation of the energy system the coming century. Modeling technological change is an important factor in energy systems modeling. Technological change may be treated as induced by climate policy or as exogenous. We investigate the importance of induced technological change (ITC) in GET-LFL, an iterative optimization model with limited foresight that includes learning-by-doing. Scenarios for stabilization of atmospheric CO2 concentrations at 400, 450, 500 and 550 ppm are studied. We find that the introduction of ITC reduces the total net present value of the abatement cost over this century by 3-9% compared to a case where technological learning is exogenous. Technology specific polices which force the introduction of fuel cell cars and solar PV in combination with ITC reduce the costs further by 4-7% and lead to significantly different technological solutions in different sectors, primarily in the transport sector.

Comparative life cycle cost assessment of painted and hot-dip galvanized bridges

The study addresses the life cycle cost assessment (LCCA) of steel bridges, focusing on the maintenance activities and the maintenance scenario. Firstly, the unit costs of maintenance activities and their durability (i.e. the time between two activities) are evaluated. Pragmatic data are provided for the environment category C4 and for three activities: Patch Up, Overcoating and Remove & Replace. A comparative LCCA for a typical hypothetic steel girder bridge is carried out, either painted or hot-dip galvanized (HDG), in the environmental class C4. The LCC versus the cumulated life is provided for both options. The initial cost of the steel unpainted option is only 50.3% of the HDG option. It is shown that after ‘Overcoating’ occurring at 18.5 years, the total Net Present Value (NPV) of the painted option surpasses that of the HDG option. A sensitivity analysis of the NPV to the cost and service life parameters, the escalation and discount rates is then performed. The discount and escalation rates, considerably influences the total LCC, following a non-linear trend. The total LCC decreases with the discount rate increasing and, conversely, increases with the escalation rate increasing. Secondly, the influence of the maintenance scenario on the total LCC is assessed based on a probabilistic approach. A permutation of the three independent maintenance activities assumed to occur six times over the life of the bridge is considered and a probability of occurrence is associated to each unique scenario. The most probable scenarios are then classified according to their NPV or achieved service life. This approach leads to the definition of a cost-effective maintenance scenario i.e. the scenario, within all the considered permutations, that has the minimum LCC in a range of lifespan. Besides, the probabilistic analysis also shows that, whatever the scenario, the return on investment period ranges between 18.5 years and 24.2 years. After that period, the HDG option becomes economic.

Multiple community energy storage planning in distribution networks using a cost-benefit analysis

This paper proposes a strategy for optimal allocation of multiple Community Energy Storage (CES) units in a distribution system with photovoltaic (PV) generation. The main contribution of this strategy is it considers all possible benefits accrued from and costs incurred by CES deployment to a utility. The benefits are gained from energy arbitrage, peaking power generation, energy loss reduction, system upgrade deferral, emission reduction and VAr support. A cost-benefit analysis is also conducted to identify the optimal Net Present Value (NPV), Discounted Payback Period (DPP) and Benefit-Cost ratio (BCR). Moreover, an optimal power factor approach is included in the analysis to dispatch CES units to improve load factors and voltage profiles. The probabilistic distribution of solar irradiance is also incorporated in the proposed strategy to consider the uncertainty of PV generation. Several sensitivity analyses are carried out to investigate the effects of PV penetration, load models and the number of CES units deployed on the profitability of CES investment. Numerical results show that the proposed strategy enables a power utility to identify the location, size and operational characteristic of CES units for maximizing the total NPV and enhancing load factors and voltage profiles.

An NPV Optimization Model for Closed-Loop Supply Chain Network Design and Planning

As the importance of forward-reverse logistics increases, attention will be focused on parameters which affect Net Present Value (NPV) of the whole system, aiming at considering the time value of money in planning. In this research, an NPV optimization model for multi-period multi-product closed-loop supply chain network design and planning is developed to maximize the total NPV. The effect of interest rates on profit and performance, the effect of the maximum return ratio on the overall NPV of the system and the effect of interest rate change on the network configuration are studied.