Mean Net Present Value Research Articles

Net metering rolling credits vs. net billing buyback: An economic analysis of a policy option proposal for photovoltaic prosumers

There is a need for a methodology that allows the prosumer to implement a policy of choosing different compensation mechanisms. Thus, we propose the economic equivalence between net metering rolling credit (NM-RC) and net billing buyback (NB-BB) by defining a breakeven price (BP) for NB-BB that equals the Net Present Value (NPV) of these two options and using Discounted Payback Time (DPBT) as a tiebreaker metric. The Brazilian scenario was used for validation. When using the retail price, the NPV values for the NM-RC mechanism were lower in all scenarios (mean NPV of US$ 3958.66 in NM-RC against US$ 4372.17 in NB-BB). It is possible to observe that the BP that equalizes the two mechanisms is generally lower than the tariff charged by the utility (mean BP is US$ 0.1111 while mean tariff is US$ 0.1838), which reveals that offering this choice option does not burden the system. The NM-RC was selected in four cities, and the NB-BB was chosen in three cities. This policy could encourage potential prosumers, who often feel reluctant to invest in PV-DG owing to the long payback period. Thus, the political schemes complementing the compensation mechanism are relevant, especially for continental-sized countries with many utilities.

Project Investment Analysis on New Oil and Gas Field Development (M-X) at Pt. PTM

PT.PTM as one of major oil and gas company in Indonesia contributes highly to fulfilling oil and gas demand in Indonesia. However, with more than 40 years of operation, PT. PTM is currently at its declining phase. Aggressive development projects have been planned to sustain PT. PTM production. The latest development project is M-X new field developments. This Final Project is intended to assess the M-X Project feasibility in terms of economic terms considering possible dynamic changes in several factors. The results of this final project will help PT.PTM management decide whether to invest in the development of the M-X field. Both Discounted and Non-Discounted Cash Flow (DCF and Non DCF) will be used in the M-X Project investment analysis. Revenue and cost breakdown will follow Production Sharing Contract (PSC) – Cost Recovery scheme as applied for PT. PTM. The Non-DCF analysis shows that 264.65 MMUSD net cash flow will be generated for the Government due to M-X development while PT. PTM will gain 49.97 MMUSD with payback period of 3 years after M-X production. DCF analysis result shows that Project Net Present Value (NPV) is 4.35 MMUSD with an IRR of 12%. Financial risk due to dynamic changes in oil and gas project is analyzed using Montecarlo simulation that resulted on 23.3% probability to have negative NPV with Mean NPV of 13.93 MMUSD. The analysis result from Non-DCF and DCF method shows positive result for PT. PTM management to proceed with investment planning for M-X field development.

A novel hybrid method for constructing resilient microalgae supply chain: Integration of n-1 contingency analysis with stochastic modelling

Microalgae biomass is currently gaining the limelight due to its flexible characteristics such as easily adaptable in cultivation environment, high lipid content that makes it suitable for biofuel processing. However, one of the main challenges faced is the resiliency of the microalgae supply chain network (MSCN) when facing external factors that can lead to supply and demand disruptions. In this study, the integration of n-1 contingency analysis with Monte Carlo simulation model is proposed to conduct resilient supply chain planning when there is supply and demand disruption (i.e., the developed supply chain is robust to fulfil all the demand even when any of the processing hubs is malfunctioning). The n-1 contingency analysis is conducted via the P-graph model framework with the integration of sustainability index —economic and environmental. Subsequently, Monte Carlo simulation model is performed for the extracted distribution network to evaluate the financial probability profile of the optimized distribution network. The collaboration between separate processing hubs is proposed to assist in meeting demand when one of the processing hubs malfunctions with the consideration of the lowest additional cost and carbon dioxide emissions pathway. The results obtained highlighted that inter-plant collaboration is able to increase the mean Net Present Value (NPV) value by up to four times and percentage standard deviation is able to be reduced by up to 49% when inter-plant collaboration is being implemented. Thus, it can be said that the inter-plant collaboration can provide a solution when production plant malfunction and also increases the cash flow in.

Comparison of PFAS soil remediation alternatives at a civilian airport using cost-benefit analysis

Contamination of soil and water systems by per- and polyfluoroalkyl substances (PFAS) due to uncontrolled use of aqueous film-forming foams (AFFFs) at firefighting training sites at civilian and military airports is a universal issue and can lead to significant human health and environmental impacts. Remediation of these sites is often complex but necessary to alleviate the PFAS burden and minimise the risks of exposure by eliminating the hotspot/source from which the PFAS spreads. This study presents a probabilistic cost-benefit analysis (CBA) for evaluating PFAS remediation alternatives, which includes monetisation of both direct costs and benefits as well as externalities. The method is applied for a case study to compare five remediation alternatives for managing PFAS contaminated soil at Stockholm Arlanda Airport in Sweden. The social profitability, or the net present value (NPV), of each remediation alternative was calculated in comparison to two reference alternatives – ‘total excavation’ of the site (Alt 0) or ‘do nothing’. Sensitivity analyses and model scenarios were tested to account for uncertainties, including small or large PFAS spreading and simulating different values for the magnitude of annual avoided cost of inaction (i.e., aggregate benefit) from PFAS remediation. In comparison to total excavation, four of the five studied remediation alternatives resulted in a positive mean NPV. Excavation and stabilization/solidification of the hotspot on-site combined with stabilization using activated carbon for the rest of site (Alt 2) had the highest NPV for both spreading scenarios, i.e., Alt 2 was the most socially profitable alternative. Simulations of the annual avoided cost of inaction enabled estimation of the breakeven point at which a remediation alternative becomes socially profitable (NPV > 0) compared to ‘do nothing’. Alt 2 had the lowest breakeven point: 7.5 and 5.75 millions of SEK/year for large and small spreading, respectively.

Addressing supply uncertainties using multi-period stochastic economic evaluation: A graph-theoretic aided element targeting approach

The challenges to commercialize biomass industry includes biomass supply shortage which is dependent on geographical location and seasonality. Most of the present biomass supply chain studies had not considered incorporation of supply chain uncertainties that may lead to overestimation of financial performance. Therefore, a hybrid framework was proposed via integration of stochastic Monte Carlo Simulation model with element targeting approach (Biomass Element Life Cycle Analysis, BELCA-P-graph model) to perform scheduling and economic analysis for the biomass supply chain. The BELCA-P-graph model aimed to generate a baseline for the feedstock ratio of each input biomass. This was then input into the stochastic model, capable of estimating the financial probability of the supply chain while incorporating supply chain uncertainties (i.e., biomass element characteristics, transportation-related parameters, raw material pricing, biomass availability, market demand, and selling price of final product). Results showed that biomass shortage had decreased the mean Net Present Value (NPV) of the base case scenario (without consideration biomass supply shortage) by 1.39%–12.21%. Storage capacity consideration had decreased the mean NPV by 11.59%–12.21%. The sensitivity analysis found that syngas demand and syngas selling price uncertainty offered significant impact on the mean NPV outcome. • Integrated Monte Carlo model with BELCA-P-graph model was presented. • Six supply chain uncertainties are modelled using Monte Carlo simulation. • Biomass shortage reduced mean NPV of base case up to 12.21%. • Implementation of storage resulted in a further reduction in mean NPV.

Cost–benefit analysis of orientation and mobility programs for adults with vision disability: a contingent valuation study

Purpose The purpose of this study is to conduct a cost–benefit analysis of orientation and mobility (O&M) programs from three perspectives: the general public, the experienced, and the potential users of O&M programs. Methods Willingness-to-pay (WTP) for O&M programs was collected via a contingent valuation survey using a double-bound dichotomous choice approach. WTP was estimated using interval regression analyses, accounting for study arm, sex, occupation, income, and self-rated health. The cost data were estimated from a service provider’s perspective. The net present value (NPV), variation if delivered by tele-O&M, was investigated. Results The adjusted mean NPV of O&M programs was $3857 (95% CI: $3760–$3954) per client, with highest NPV from the general public ($4289, 95% CI: $4185–$4392), followed by the experienced users ($3158, 95% CI: $2897–$3419) and the potential users ($2867, 95% CI: $2680–$3054). The NPV reached break-even for tele-O&M. Conclusions There was strong community support for investment into O&M programs considering benefits for clients over and above the cost of providing the services. Implications for rehabilitation This study demonstrates the feasibility of using cost–benefit analysis with a contingent valuation approach to economically assess a rehabilitation intervention, where its multi-dimensional benefits cannot be fully captured by a conventional appraisal technique such as cost-effectiveness analysis. The high willingness-to-pay (WTP) values amongst the general public suggests that Australians perceive government’s investment in orientation and mobility (O&M) rehabilitation as value for money and that individuals would be prepared to contribute to its costs. The lower WTP for O&M partially delivered via tele-practice (tele-O&M) indicates a lower acceptance of this innovation in comparison with the traditional face-to-face O&M. The estimated net present values of O&M programs, positive for traditional O&M and break-even for tele-O&M, can be of assistance to service planning and investment decisions within the Australian context.

Towards effective reforestation: growth and commercial value of four commonly planted tropical timber species on infertile soils in Panama

Finding suitable tree species that not only grow well on nutrient poor soils but are also safe financial investments is one of the major obstacles to successful reforestation efforts in the tropics. Our study compared the financial viability and growth of valuable timber species in monocultures and mixtures on infertile soils. Our work shows the extraordinary growth in volume and value of Dalbergia retusa and Terminalia amazonia while underscoring the poor financial viability of Tectona grandis and Pachira quinata, all commonly planted timber species in Panama and much of Central and South America. Using Bayesian statistics, our predictions show that T. amazonia monocultures could reach nearly 200 m3 ha−1 of merchantable volume after 30 years compared to the ~ 40 m3 ha−1 that T. grandis could accumulate in the same time frame. While D. retusa monocultures did not have the highest predicted merchantable volumes of all the species, it did have the highest predicted net present value (NPV), with a predicted mean NPV of > US$97,000 ha−1, quadrupling the species with the next highest monoculture’s NPV, T. amazonia monocultures (~ US$20,000 ha−1). Our work emphasizes that reforestation can be financially viable on low nutrient soils, even in the absence of fertilization or other silvicultural manipulations, if species selection and site are carefully considered and matched.

Financial Feasibility Study and Maintenance Strategy of Articulated Dump Truck (A Case Study of PT. KLM)

Beyond 2021, the challenging difficulties would need coal mining firms, particularly PT.KLM, to increase operational efficiency. The author conducts a root cause analysis in order to determine one of the causes of the business's inefficiencies. At the time, one of PT. KLM's inefficiencies is a result of the articulated dump truck’s (ADT) performance, which fell short of the projected aim. The author provides two alternatives for ADT at PT. KLM, starting from Certified Rebuild Machine (CRM) then Full Maintenance Service (FMS) and CRM then Full Maintenance Contract (FMC). Feasibility studies are undertaken in order to determine the most viable alternative. Both sensitivity analysis and Monte Carlo simulation is conducted to determine the input variable's influence on changes in NPV (Net Present Value) and to generate a variety of possible outcomes and probabilities of various situations. The author proposes scenario 2 as the optimal scenario with the highest NPV based on the study. Additionally, following the Monte Carlo simulation, the mean NPV + minimum NPV and mean NPV + maximum NPV values for scenario 2 are the highest in comparison to the other scenario.

Assessment of the feasibility of the production of alternative jet fuel and diesel using catalytic hydrothermolysis technology: a stochastic techno‐economic analysis

AbstractThe use of renewable fuels has emerged as an important method for reducing fossil‐fuel consumption. Different pathway technologies can lead to different fuel products. The feasibility of many such pathways has been analyzed with mixed results but new technologies are being developed continuously. This study uses stochastic analysis to determine the feasibility of producing biofuels from carinata oil using catalytic hydrothermolysis (CH) technology. We perform the analysis with and without government incentive programs. We also address uncertainties in input costs and government incentives. The study analyzes a pioneer greenfield plant. The results show that the mean net present value (NPV) without government incentives is −$924.4 million. Ninety percent of the simulated NPV is between −$1040 million and −$810 million, which indicates a 100% probability of loss. The mean breakeven price of jet fuel is $4.72 gal–1. With government incentives including Renewable Identification Number (RIN) and Low Carbon Fuel Standard (LCFS), the mean NPV is $62.3, and the probability of loss is reduced to 21%, which makes the process much more financially feasible. © 2021 Society of Chemical Industry and John Wiley & Sons, Ltd

Economic Analysis of a Massively Populated Internet of Things System: An Agent-Based Simulation Approach

With 212 locations serving a geographical area three times the size of the state of Texas, our international client has been experiencing unexpected failures in his portfolio of thousands of refrigeration units, resulting in an associated loss of $1.4 million. These losses have been attributed to lapses in food quality caused by improper refrigeration, and the adopted reactive maintenance strategy of the refrigeration units. To assist our client in complying with the cold-chain regulations of food safety and mitigate such losses, the agent-based simulation was used to model and assess the viability of two distinct massively populated Internet of Things (IoT) alternatives. The first (alternative A) involves monitoring the availability of each of the refrigeration units and signaling for the initiation of repair processes and food removal from failed units when the cooling temperature differs from a specified threshold and a more sophisticated and expensive alternative B that involves the adoption of the added capability of a condition-based predictive maintenance strategy to reduce unplanned downtime and mitigate or eliminate the causes of failure. Using historical data, a simulation of the current operations was first modeled, validated, and then augmented with capabilities to address the operational characteristics of the proposed IoT implementations. Results of financial analysis results including the probabilistic risk analyses, that account for the variations and the probability distributions of the assumption parameters, showed alternative A to be superior, with a mean net present value (NPV) of $416,703, and a modified IRR of about 18.77% exceeding the project cost of capital of 12.80% with complete certainty. The client was also advised that a 30% reduction in the acquisition cost would make investment B as viable as investment A.

Potential economic impact of carbon sequestration in coffee agroforestry systems

Conventional agriculture is a significant driver of ecosystem service loss. In contrast, agroforestry is a land use option that can restore ecosystem services, such as carbon sequestration. Using field data from small proprieties we aimed to quantify carbon sequestration and its potential economic impact in coffee agroforestry systems (CAS) in Brazil. Allometric equations were used to estimate carbon stock and tree biomass, while net present value (NPV), internal rate of return (IRR), and payback period were used as economic feasibility parameters in a 16-year modeled scenario. Discount rate and carbon price applied was 6% and US$ 5.1 Mg CO2e-1, respectively. Total mean carbon stock in two years old and 16 years old CAS were 1.38 ± 0.63 Mg C ha-1 and 59.69 ± 32.63 Mg C ha-1, respectively. All CAS presented payback periods of two years, with a mean NPV of US$ 50,585.91 and a mean IRR of 89.93%. Carbon revenue impact on NPV and IRR was US$ 148.80 and 0.24%, respectively. Our results indicate that carbon sequestration economic impact does not encourage the adoption of coffee agroforestry systems. However, these systems can be an economically viable land use option that sequester large amounts of carbon comparable to forest patches.

Robust optimization formulations for waterflooding management under geological uncertainties

In oil reservoir management, one of the major challenges is the search for the best production plan under geological uncertainties. One way to conduct optimal management of reservoirs under uncertainty is through robust optimization, which uses a set of realizations to approximate some statistics of the reservoir properties. The statistics considered here are the mean and standard deviation of the net present value (NPV). In this work, two different formulations for the optimal robust management of oil reservoirs are considered using a unique objective function. One formulation is also presented in a multi-objective context. All of these are employed to solve two benchmark problems. As all the proposed formulations are very computationally intensive, surrogate models are used for the several function calls required in both optimization and uncertainty propagation. The results obtained by the proposed strategies proved to be efficient for both single and multi-objective problems. For the two studied problems, using 500 and 100 realizations, respectively, the obtained results, when compared with the ones obtained by reactive control strategy, show an increase of mean NPV more than 10% in average. The multi-objective optimization results for both investigated reservoirs show an optimal robust Pareto front that can be used to make a trade-off between the mean of NPV and the mean of water injection. This work shows that surrogate models, a special subset of the full set of reservoir realizations and parallel processing should be used for the application in robust managements of practical reservoir engineering problems.

Comprehensive comparative economic evaluation of woody biomass energy from silvicultural fuel treatments

Fuel treatments are used in overstocked, fire-prone forests to alter wildfire behavior and reduce fire risk. Some of the benefits they provide are not captured in markets, and therefore represent unaccounted environmental externalities that can lead to inefficient decision making. This study uses a replicable method to integrate market and nonmarket economic values into a comprehensive economic evaluation of fuel treatment and bioenergy production using a case study of ponderosa pine and mixed-conifer forests in Colorado's wildland-urban interface. Treatment costs and people's willingness to pay for better forest health, lower likelihood of wildfire, improved air quality, and expanded renewable energy production are incorporated into techno-economic analysis of biopower production. Results show that fuel treatments are likely to be undervalued when evaluated strictly on a financial basis. Under the standard practice of disposing of treatment residues through pile-burning, net present value (NPV) of fuel treatment on 138,034 ha over 20 years is -$275 million, without consideration of nonmarket benefits. If nonmarket benefits associated with forest health, wildfire likelihood and air quality are included, NPV improves to -$116 million. Without the consideration of nonmarket benefits, when treatment resides are used for biopower production, NPV is -$178 million, with net cost savings compared to pile burning attributable to reduced biomass disposal costs and electricity revenue. Accounting for additional air quality benefits and nonmarket value associated with renewable energy, the bioenergy scenario improves NPV to -$25 million, with 27.7% of outcomes having positive NPV. The impact of additional nonmarket values and potential revenues from timber harvest are discussed, and are likely to make mean NPV positive for this scenario.

Optimal planning of electricity storage to minimize operating reserve requirements in an isolated island grid

Electrical energy storage (EES) constitutes a potential candidate capable of regulating the power generation to match the loads via time-shifting. Optimally planned, EES facilities can meet the increasing requirement of reserves to manage the variability and uncertainty of renewable energy sources (RES) whilst improving the system operation efficiency and economics. In this work, the impact of intermittent RES on total production cost (TPC) is evaluated in the presence and absence of storage, using annual data regarding the non-interconnected power system of the island of Cyprus. Performing weekly simulations for the entire year of 2017, TPC is computed by solving the unit commitment based on a constrained Lagrange Relaxation method. Seven selected EES technologies are modeled and evaluated via a life-cycle cost analysis, based on the most realistic technical and cost data found in the literature. The results derived from the uncertainty analysis performed, show that zinc-air (Zn-air) battery offers the highest net present value (NPV). Lead-acid (Pb-acid) and sodium-sulfur (Na-S) are considered viable solutions in terms of mean NPV and investment risk. Lithium-ion (Li-ion) battery exhibits a particularly expensive choice. Dominated by its increased capital cost which still governs its overall cost performance Li-ion achieves a negative mean NPV far below zero. However, to strengthen the benefits derived from EES integration, further research and development is needed improving the performance and costs of storage. The uncertainty governing the majority of EES technologies, in turn, will be reduced, increasing their participation and RES contribution in autonomous power system operations.

Techno-economic feasibility of power to gas–oxy-fuel boiler hybrid system under uncertainty

One of the main challenges associated with utilisation of the renewable energy is the need for energy storage to handle its intermittent nature. Power-to-Gas (PtG) represents a promising option to foster the conversion of renewable electricity into energy carriers that may attend electrical, thermal, or mechanical needs on-demand. This work aimed to incorporate a stochastic approach (Artificial Neural Network combined with Monte Carlo simulations) into the thermodynamic and economic analysis of the PtG process hybridized with an oxy-fuel boiler (modelled in Aspen Plus®). Such approach generated probability density curves for the key techno-economic performance indicators of the PtG process. Results showed that the mean utilisation of electricity from RES, accounting for the chemical energy in SNG and heat from methanators, reached 62.6%. Besides, the probability that the discounted cash flow is positive was estimated to be only 13.4%, under the set of conditions considered in the work. This work also showed that in order to make the mean net present value positive, subsidies of 68 €/MWelh are required (with respect to the electricity consumed by PtG process from RES). This figure is similar to the financial aids received by other technologies in the current economic environment.

Renewable energy integration through optimal unit commitment and electricity storage in weak power networks

ABSTRACTIn this work, the impact of intermittent renewable energy sources on total production cost is evaluated, using annual data regarding the isolated power system of the island of Cyprus. Once electrical energy storage (EES) is identified as an approach enhancing flexibility and reliability, the selected EES facilities are modelled and evaluated via a life-cycle cost analysis, based on the most realistic characteristics and cost metrics found in the literature. The results derived from the uncertainty analysis performed, show that vanadium-redox flow battery provides the highest net present value (NPV). However, sodium-sulfur battery system offers the most secure investment in terms of uncertainty range and mean value, followed by lead-acid battery system. Lithium-ion battery system exhibits expensive capital cost which still governs its overall cost performance achieving a negative mean NPV far below zero.

Financial viability of biofuel and biochar production from forest biomass in the face of market price volatility and uncertainty

A comparative techno-economic analysis of two different thermochemical biomass conversion pathways was conducted to examine the effects of fuel price and other variables on project financial performance. Monte Carlo simulation was used to quantify the effects of uncertainty and volatility of ten critical variables: biofuel, biochar and feedstock prices, discount rate, capital investment, labor cost, loan terms, feedstock drying, and biofuel and biochar conversion rates. Market prices for biofuel and biochar have the largest impact on net present value (NPV) of any variable considered, due in part to the high levels of uncertainty associated with future prices of both. Across the ranges of input values for these variables in simulation analysis, hearth-based pyrolysis biochar production had the highest likelihood of profitability with a mean NPV of $41.5 million and only 20% of outcomes resulting in a net loss, while 68% of outcomes for auger-based biochar-biofuel coproduction represented a financial loss, including a mean NPV of -$24.2 million. However, when additional revenue from Renewable Identification Numbers (RINs) credits generated by biofuel production is considered, financial outcomes of biochar-biofuel coproduction improve to 50% likelihood of experiencing a net loss. Findings of the very strong impact of market prices on financial outcomes, relative to other important technical and economic variables, can inform effective targeting of future renewable energy policy, as well as the design of future techno-economic analyses, which do not currently focus on the effect of market prices on profitability.

Technoeconomic and Policy Drivers of Project Performance for Bioenergy Alternatives Using Biomass from Beetle-Killed Trees

As a result of widespread mortality from beetle infestation in the forests of the western United States, there are substantial stocks of biomass suitable as a feedstock for energy production. This study explored the financial viability of four production pathway scenarios for the conversion of beetle-killed pine to bioenergy and bioproducts in the Rocky Mountains. Monte Carlo simulation using data obtained from planned and existing projects was used to account for uncertainty in key technoeconomic variables and to provide distributions of project net present value (NPV), as well as for sensitivity analysis of key economic and production variables. Over a 20-year project period, results for base case scenarios reveal mean NPV ranging from a low of −$8.3 million for electric power production to a high of $76.0 million for liquid biofuel with a biochar co-product. However, under simulation, all scenarios had conditions resulting in both positive and negative NPV. NPV ranged from −$74.5 million to $51.4 million for electric power, and from −$21.6 million to $246.3 million for liquid biofuels. The potential effects of economic trends and public policies that aim to promote renewable energy and biomass utilization are discussed for each production pathway. Because the factors that most strongly affect financial viability differ across projects, the likely effects of particular types of policies are also shown to vary substantially.

Techno-economic and greenhouse gas savings assessment of decentralized biomass gasification for electrifying the rural areas of Indonesia

This study explored the feasibility of decentralized gasification of oil palm biomass in Indonesia to relieve its over-dependence on fossil fuel-based power generation and facilitate the electrification of its rural areas. The techno-feasibility of the gasification of oil palm biomass was first evaluated by reviewing existing literature. Subsequently, two scenarios (V1 and V2, and M1 and M2) were proposed regarding the use cases of the village and mill, respectively. The capacity of the gasification systems in the V1 and M1 scenarios are determined by the total amount of oil palm biomass available in the village and mill, respectively. The capacity of the gasification systems in the V2 and M2 scenarios is determined by the respective electricity demand of the village and mill. The global warming impact and economic feasibility (net present value (NPV) and levelized cost of electricity (LCOE)) of the proposed systems were compared with that of the current practices (diesel generator for the village use case and biomass boiler combustion for the mill use case) using life cycle assessment (LCA) and cost-benefit analysis (CBA). Under the current daily demand per household (0.4 kWh), deploying the V2 system in 104 villages with 500 households each could save up to 17.9 thousand tons of CO2-eq per year compared to the current diesel-based practice. If the electricity could be fed into the national grid, the M1 system with 100% capacity factor could provide yearly GHG emissions mitigation of 5.8 × 104 ton CO2-eq, relative to the current boiler combustion-based reference scenario. M1 had a positive mean NPV if the electricity could be fed into the national grid, while M2 had a positive mean NPV at the biochar price of 500 USD/ton. Under the current electricity tariff (ET) (0.11 kWh) and the biochar price of 2650 USD/ton, daily household demands of 2 and 1.8 kWh were required to reach the break-even point of the mean NPV for the V2 system for the cases of 300 and 500 households, respectively. The average LCOE of V2 is approximately one-fourth that of the reference scenario, while the average LCOE of V1 is larger than that of the reference scenario. The average LCOE of M1 decreased to around 0.06 USD/kWh for the case of a 100% capacity factor. Sensitivity analysis showed that the capital cost of gasification system and its overall electrical efficiency had the most significant effects on the NPV. Finally, practical system deployment was discussed, with consideration of policy formulation and fiscal incentives.

Multi-objective optimization for rapid and robust optimal oilfield development under geological uncertainty

Optimal well placement is critical to oil and gas field development. Typical workflows involve procedures to place a new well or a group of new wells in a reservoir in order to maximize some pre-defined reservoir performance metric. However, there are two main drawbacks with these traditional optimization approaches: First, the impact of geological uncertainty is often neglected or there may be no framework to include geological uncertainty. Second, traditional optimization techniques normally cannot meet the requirement of optimizing two or more conflicting objectives simultaneously—this may be useful when maximizing oil recovery while also minimizing water production. Consequently, in recent years, multiple objective optimization to obtain robust solutions that minimize the decision risk under geological uncertainty becomes a topic of renewed interest. Therefore, in this work, we develop a new work flow for well placement optimization while considering geological uncertainty in reservoir models. In general, when considering geological uncertainty, the primary goal is to maximize the mean net present value (NPV) over all realizations. However, restricting the search to simply maximizing the mean NPV may be inappropriate or inadequate for decision-making. A more reasonable choice is to maximize the mean NPV while minimizing the spread of the optimal NPV’s obtained for each realization. Therefore, in this work, we apply multi-objective optimization techniques to maximize the mean and minimize the variance of NPV values over all geological realizations to provide robust well placement solutions for decision-makers to select according to their risk attitude towards field development plans.