Economic Metrics Research Articles

Impact of Electrical Topology, Capacity Factor and Line Length on Economic Performance of Offshore Wind Investments

In this study, an economic performance assessment of offshore wind investments is investigated through electrical topology, capacity factor and line length. First, annual energy yield production and electrical system losses for AC and DC offshore wind configurations are estimated by using Weibull probability distributions of wind speed. A cost model for calculating core energy economic metrics for offshore wind environment is developed by using a discount cash flow analysis. A case study is then conducted for a projected offshore wind farm (OWF) rated 100 MW and 300 MW sizes situated in the Aegean sea. Finally, a sensitivity analysis is performed for AC and DC OWFs with three different capacity factors (e.g., 45%, 55% and 60%) and various transmission line lengths ranging from 20 km to 120 km. The OWF is found to be economically viable for both AC and DC configurations with the estimated levelized cost of electricity (LCOE) ranging from 88.34 $/MWh to 113.76 $/MWh and from 97.61 $/MWh to 126.60 $/MWh, respectively. LCOEs for both options slightly change even though the wind farm size was increased three-fold. The sensitivity analysis reveals that, for further offshore locations with higher capacity factors, the superiority of AC configuration over the DC option in terms of LCOE reduces while the advantage of DC configuration over the AC option in terms of electrical losses is significant. Losses in the AC and DC configurations range from 3.75% to 5.86% and 3.75% to 5.34%, respectively, while LCOEs vary between 59.90 $/MWh and 113.76 $/MWh for the AC configuration and 66.21 $/MWh and 124.15 $/MWh for the DC configuration. Capacity factor was found to be more sensitive in LCOE estimation compared to transmission line length while line length is more sensitive in losses estimation compared to capacity factor.

A multi-criteria decision support tool for biorefinery siting: Using economic, environmental, and social metrics for a refined siting analysis

Facility siting has traditionally been performed using economic metrics alone to determine suitable locations for a new facility. In this era of climate change concerns and political discord, a more holistic approach to biorefinery siting may yield alternative locations that meet stakeholder goals for community acceptance and reduced environmental impacts. A multi-criteria decision support tool (DST) that incorporates economic, environmental, and social metrics concurrently is introduced to assess the repurpose potential of existing facilities as a wood-based biorefinery. Economic siting criteria are represented by biorefinery operational cost components that vary geospatially. The environmental criterion is the Global Warming Potential of the supply chain, as measured through greenhouse gases emitted from the feedstock procurement, preprocessing, and transport equipment. Social criteria are represented by 1) the number of regional jobs created through the installation of a biofuel supply chain, and 2) county-level social assets that may influence biorefinery project success. Weights and scale values are derived for each set of metrics. An overall facility score is produced by summing the three metric scores. Additionally, overall user-defined metric weights are used to adjust the importance of the three metrics, thus altering the overall facility scores. The DST is applied to a case study in Western Oregon and Western Washington to refine a list of candidate pulp mills down to a select few for further investigation. It was found that the mills scored differently when overall metric weights were adjusted. Therefore, different stakeholder preferences may yield a different priority list of facilities.

Prototype Compressed Air Vehicle: An alternative to meet energy demand mitigation for sustainable development

The urgent need of eco-friendly transport vehicles and alternate source of energy other than fossil fuels draw attention of entire world towards the sustainable development of green technologies due to sudden climate changes and global warming. In the face of the climate crisis, petroleum dependence, and volatile gasoline prices, it is imperative to explore possible opportunities in unconventional alternative-fuel vehicles. In this due course Compressed Air Vehicles (CAV) is a thrust area of research. CAV utilizes high pressure energy of air from compressed form which is converted into mechanical output. For the same Compressed air is stored in a cylinder and released by a nozzle connected through the piping. Compressed air passing through nozzle strikes the blade with a very high velocity, resulting in the rotation of prime mover. To produce a high torque and to reduce the rpm, this prime mover is coupled with gear train which is further connected with the axle. So that rotational energy of prime mover is converted into mechanical output easily and efficiently i.e. the motion of CAV. Based this protocol a prototype compressed air vehicle was designed and developed. The efficiency of the prototype model under the different condition of load and speed in rpm was studied. Results show that although the CAV is a bold, unconventional solution for today’s transportation challenges, it is ultimately not workable, and compares poorly with gasoline and electric vehicles in all environmental and economic metrics.

Framework for Design Under Uncertainty Including Inherent Safety, Environmental Assessment, and Economic Performance of Chemical Processes

Typically, analysis and design of chemical processes is carried out considering average values of inherently uncertain data that pertain to key design and process variables that shape its performance. In this work, a methodology that includes uncertainty in process safety, an item commonly addressed after the final design has been achieved, along with environmental and economic metrics of the process at the design stage is presented. The approach is based on the use of Monte Carlo simulation techniques to explicitly include uncertainty in the input variables and propagate it through the model. The proposed approach generates a range of performance outcomes through distribution profiles that can be probabilistically characterized, and zones of downside risks and upside opportunities can be identified. A case study that shows the application of the proposed methodology based on the production of ethylene from natural gas was conducted. The results show that although the safest design presented a lower return on investment (ROI), the probability of this scenario to materialize was relatively low, suggesting that a more desirable ROI value for that design would be likely.

Economic-Mathematical Model of Telecommunication System, Which is Being Developed for a High-Tech Company

The article proposes a model of a telecommunication system that allows to conduct a joint study of its functional and cost characteristics. For this model, three technical indicators and three economic metrics are used. They allow to explore the basic characteristics of a telecommunication network under normal operating conditions and when a critical situation of a different nature occurs. In this case, the numerical values of the attributes included in the bundle of the three technical indicators and the three economic metrics differ only. Consequently, the model itself remains unchanged. A methodology for analyzing the proposed model is developed. Two examples of the application of the proposed methodology are given. It is shown that in critical situations, which are often associated with a sharp decrease in the available resources of the telecommunication network with a simultaneous sharp increase in traffic, it is advisable to use methods of interdisciplinary approach. Such methods, as a rule, will significantly improve the effectiveness of further research.

Influence of hydrologically based environmental flow methods on flow alteration and energy production in a run-of-river hydropower plant

Increasing demand for renewable energy has led to a massive expansion of the small-scale hydropower plants and particularly those of run-of-river type. Meanwhile, the awareness concerning river conservation and restoration is being continually increasing. This study focuses on the assessment of environmental flows releases and energy production considering eight hydrological based environmental flows methods. The main goal of this study is to propose a practical approach that allows for quick estimation of the environmental flows releases and alteration due to water diversion for energy production, in the case of run-of-river hydropower plants. For this purpose, the indicators of hydrological alteration approach was applied to characterise flow alteration, and three economic metrics were used to assess the impact on energy production. Downstream Diversion Index was proposed to characterise the degree of water diversion due to energy production. Also, five new simplified indexes derived from indicators of hydrologic alteration approach were proposed to simplify flow alteration characterisation. The results of this study showed that a minor reduction of energy production could significantly improve environmental flows releases for the riverine ecosystem needs. Particularly, proportional dynamic approaches maximise energy production and imposes relatively less flow alteration. Also, it was concluded that following flow parameters: frequency, timing, duration and rates, can be maintained close to the natural regime without necessarily reducing the energy production by choosing the right environmental flows method. Finally, the methodology developed in this study can be a useful decision-making tool to help water managers, by including other relevant information also (i.e., biological, water supply, irrigation, and social) to choose the most appropriate method for the environmental flows determination in the case of run-of-river hydropower plants.

Alaska's community development quota program: A complex institution affecting rural communities in disparate ways

The Western Alaska Community Development Quota (CDQ) program created six regional CDQ groups, each of which were given fisheries quota to manage on behalf of qualifying rural Alaskan communities. Since its inception in the 1990s, the program has created dozens of jobs and millions of dollars' worth of revenue and is widely considered an example of successful community development. However, in mixed economies like those of the rural Alaskan villages covered by the CDQ program, economic metrics often fail to capture changes in non-economic factors of critical importance to residents. To address this lacuna, we examined how CDQ has influenced community well-being in four communities associated with four different CDQ groups. Interviews and focus groups in these communities indicated that CDQ's influence on well-being varied greatly. In some communities, CDQ increased local autonomy and, hence, well-being. In other communities, residents felt disenfranchised; control of their resources rested in urban board rooms to which they struggled to gain access. These findings indicate that power dynamics built into the design of the CDQ program have important ramifications for community well-being and that future development programs in fisheries management could benefit from better understandings of socio-cultural concepts such as well-being, in both program design and assessment stages.

Toward a systems approach to social impact assessment

Purpose The purpose of the paper is to propose a systems change lens to current approaches to assessing social impact in social ventures. Many existing tools for measuring social impact are limited in their capacity to assess the inherent complexities and interconnected nature of the work done by social enterprises. Design/methodology/approach The paper uses in-depth interviews with sector experts to gain insights into their needs related to impact assessment, as well as issues they face when attempting to understand and measure their impact. Findings Expert interviews provide insights into how social impact occurs through interconnected systems. It also highlights the need for impact assessment to better consider interaction within systems and networks. Results support previous work concerning the need for methods that can better account for complexity, interacting problems and the place of power in influencing actions. Research limitations/implications Following results from interviews and review of existing literature, symbolic interactionism and Social Worlds/Arenas theories are used to gain insight as to how impact can be conceptualized in terms of systemic shifts in social equilibria. The model proposes to capture the contested definitions of problems and their negotiation in social structures. Originality/value Grounded in sociological theory, the model brings a new theoretical approach to social impact assessment, one that provides a different view of social structures than existing models that are grounded in economic metrics. The proposed model, therefore, provides a new lens for the detailed assessment of the complex interactions between systems.

Development of a roadmap for dynamic process intensification by using a dynamic, data-driven optimization approach

The concept of process intensification involves any improvement in the process or equipment that leads to reduced cost and emissions, increased efficiency, or improved productivity. This paper proposes a novel dynamic process intensification approach that results in similar benefits as the traditional process intensification. The proposed approach utilizes a dynamic optimization approach in improving the operation of a plant. The main contribution of this work includes the development of a dynamic particle swarm optimization algorithm and a data-driven model of the plant while the operational and economical metrics of the plant are compared to the same system when steady-state optimization is practiced. Additionally, a roadmap is developed that enables the practitioners to apply the proposed dynamic process intensification concept to any similar systems. A CSTR reactor is used as the case study to demonstrate the benefits of dynamic process intensification over a static approach. The results demonstrate that a dynamic, data-driven optimization algorithm can achieve similar profit as gradient-based optimization approaches. Additionally, a 59.9% increase in profitability of the plant is observed when a dynamic approach is used for optimization, as opposed to a steady-state approach. This is well-aligned with the targets pursued from the traditional process intensification concepts.

A Non-Cooperative Framework for Coordinating a Neighborhood of Distributed Prosumers

This paper introduces a scalable framework to coordinate the net load scheduling, sharing, and matching in a neighborhood of residential prosumers connected to the grid. As the prosumers are equipped with smart appliances, photovoltaic panels, and battery energy storage systems, they take advantage of their consumption, generation, and storage flexibilities to exchange energy with neighboring prosumers through negotiating on the amount of energy and its price with an aggregator. The proposed framework comprises two separate multi-objective mixed integer nonlinear programming optimization models for prosumers and the aggregator. Prosumers’ objective is to maximize the comfort level and minimize the electricity cost at each instant of time, while aggregator intends to maximize its profit and minimize the grid burden by matching prosumers’ supply and demand. The evolutionary nondominated sorting genetic algorithm-III (NSGA-III) is employed to generate a set of feasible nondominated solutions to the optimization problem of each individual prosumer and the aggregator. As a bilateral negotiation between each prosumer and the aggregator results in significant computational and communication overhead, a virtual power plant is introduced as an intermediator on behalf of all prosumers to proceed the negotiation with the aggregator in a privacy-preserving noncooperative environment, where no private information is shared. Hence, an automated negotiation approach is embedded in the framework, which enables the negotiators to reactively negotiate on concurrent power and price using private utility functions and preferences. To converge to an acceptable agreement, the negotiation approach follows an alternating-offer production protocol and a reactive utility value concession strategy. The effectiveness of the framework is evaluated by several economic and environmental assessment metrics through a variety of numerical simulations.

The economic and environmental performance of biomass as an “intermediate” resource for power production

Electricity powered by biomass is expanding. We examine four recent biopower plants and global benchmarks to assess their overall performance, confirming the characterization of biomass as an “intermediate” resource for power production. Electricity from biomass is more expensive than energy efficiency, natural gas, wind, or solar but substantially less expensive than new coal or nuclear plants. Compared to coal and natural gas per MWh produced, the NOx and SO2 emissions of biopower are also intermediate. We confirm that current investments in biopower can be attributed to an array of stakeholder value propositions extending beyond basic economic and environmental metrics.

Techno-economic viability of off-grid standalone PV-powered LED street lighting system in Lagos, Nigeria

The majority of the street lighting systems in Nigeria are predominantly composed of inefficient lighting fixtures powered either by the grid or diesel generators. Due to the epileptic nature of the grid and the fluctuations in diesel fuel pump prices, these methods for powering street lights are neither reliable nor sustainable. Use of energy-efficient lamps and photovoltaic (PV) panels has the tendency to reduce energy consumption and emissions. This study therefore presents a techno-economic analysis of deploying energy-efficient PV-powered street lighting systems using simple economic metrics. Environmental benefits of the proposed systems were also assessed by analyzing the reduction in CO2 emission as well as the equivalent of oil saved. Relative to the existing grid-powered metal halide system, installation of LED PV-powered street light reduced energy consumption by at least 80%, while the grid-powered LED configuration reduced energy consumption by 80%. Economic analysis shows that the simple payback period (SPP) for the LED PV-powered technology was less than three years, while that of the grid-powered LED system was less than two years. The LED PV-powered technology had the highest CO2 emission reduction of approximately 225 tonnes annually. Overall, street lighting using PV-powered and grid-powered LED is economically viable considering the payback time, net present value (NPV) and lifetime.

Stock Portfolio Prediction by Multi-Target Decision Support

Investing in the stock market is a complex process due to its high volatility caused by factors as exchange rates, political events, inflation and the market history. To support investor's decisions, the prediction of future stock price and economic metrics is valuable. With the hypothesis that there is a relation among investment performance indicators, the goal of this paper was exploring multi-target regression (MTR) methods to estimate 6 different indicators and finding out the method that would best suit in an automated prediction tool for decision support regarding predictive performance. The experiments were based on 4 datasets, corresponding to 4 different time periods, composed of 63 combinations of weights of stock-picking concepts each, simulated in the US stock market. We compared traditional machine learning approaches with seven state-of-the-art MTR solutions: Stacked Single Target, Ensemble of Regressor Chains, Deep Structure for Tracking Asynchronous Regressor Stacking, Deep Regressor Stacking, Multi-output Tree Chaining, Multi-target Augment Stacking and Multi-output Random Forest (MORF). With the exception of MORF, traditional approaches and the MTR methods were evaluated with Extreme Gradient Boosting, Random Forest and Support Vector Machine regressors. By means of extensive experimental evaluation, our results showed that the most recent MTR solutions can achieve suitable predictive performance, improving all the scenarios (14.70% in the best one, considering all target variables and periods). In this sense, MTR is a proper strategy for building stock market decision support system based on prediction models.

What works in tropical forest conservation, and what does not: Effectiveness of four strategies in terms of environmental, social, and economic outcomes

Tropical forests and their biodiversity are disappearing, despite decades of conservation efforts. Are we now in a position to understand whether some conservation strategies work better while others consistently fail in protecting tropical forests? We searched the literature to evaluate four mainstream strategies (forest certification and reduced impact logging, payments for ecosystem services, protected areas, community forest management) in terms of 35 environmental, social, and economic metrics. We evaluated whether applying the strategy improved, left unchanged, or worsened the conservation metrics and we created an interactive platform to view the data. We concluded that (a) the scientific literature on the effectiveness of conservation strategies in tropical forests is still vastly inadequate, due to poor design, lack of scope, and too few examples; (b) the effects of conservation on biodiversity and the economic outcomes of conservation are particularly understudied; and (c) all strategies fail at least some of the times, but all of them succeed at least some times. Our recommendation is that each new instance of implementing a given strategy should consider in detail, at the very least, the negative evidence on the given strategy, in order to avoid repeating the same mistakes. We introduce an interactive, dynamic platform to host various types of conservation effectiveness evidence.

Network-Based Financial Forecasting: A Statistical and Economic Analysis

One of the main challenges facing researchers and industry professionals for decades is the successful prediction of asset returns. This paper enriches this endeavor by an in-depth analysis of topological metrics of correlation networks applied to financial forecasting. While academic research often focuses on statistical performance metrics, industry professionals are more interested in the economic value-added of competing forecasting approaches. Since statistical significance does not automatically imply economic significance, this article devotes attention to both types of performance metrics. We show that the benchmark mean model is indeed difficult to beat when it comes to statistical performance metrics. However, considering economic metrics, network-based predictors generate a clear value-added, which also applies to the multi risky asset allocation dimension.

Deliverology in Community Economic Development: Enhancing Results Frameworks and Performance Measurement

<p> </p><p><span> </span>This paper will assess the viability of implementing an alternative delivery model and performance measurement framework – commonly known as ‘deliverology’ – at the level of community economic development. First, a review of relevant literature on traditional economic development delivery models and performance metrics is conducted to determine strengths and weaknesses. Next, a deliverology approach is defined and analyzed to determine whether such a model can address the weaknesses of more traditional approaches. The results indicate that a deliverology approach has many potential advantages for economic development service delivery and addresses many of the weaknesses of current models and frameworks. Since deliverology remains rather new compared to more traditional approaches, further research in terms of a case study in a large urban municipality is recommended as a way to test the applicability of deliverology to community economic development. </p><p><strong>Keywords: </strong>deliverology, performance measurement, results and delivery framework, community economic development, service delivery model</p>

Single-Tier or Dual-Tier Tax Systems: Implication for Petroleum Project Economics in Nigeria

Summary In this work we evaluate the effects of the single-tier tax system (STS) at its current rate in comparison to the proposed dual-tier tax system (DTS) in the National Petroleum Fiscal Policy in Nigeria on project economic performance. We also expound on the arguments between two schools of thought (single tax and dual tax proponents) toward understanding the rationale underlying the divergent viewpoints. The methodological approach applies the discounted cash flow modeling framework to evaluate the performances of terrain-based projects using selected metrics, such as internal rate of return, discounted payout, net present value, and government take (GT) under the two tax systems. This approach calibrates the unit technical cost for typical deepwater projects in Nigeria and imposes the current and proposed fiscal terms. Varying cost treatment options and alternative allowables//incentives are investigated in the modeling framework using global best practices. We conclude that whichever tax system is adopted, it is possible to achieve equivalent economic metrics. However, the DTS presents a better, more flexible option over the STS because one of the split rates—especially the hydrocarbon resource tax—could serve as an instrument to incentivize investment, promote conservation, and expand the resource base through technology innovation more easily without denying the mineral owner an outright revenue through taxation. In a classical case such as Nigeria, in which the national fiscal budget is largely financed using hydrocarbon revenue, the DTS seemingly offers a better option for revenue sharing among the stakeholders—the resource owners and the Federal Government—than the current single upstream tax system. Our discussion in this paper bridges the gap between the divergent viewpoints on the taxation system in Nigeria by proffering a pathway. We suggest that the overall objectives of stakeholders could be achieved using either STS or DTS metrics if the mechanics in designing the fiscal system is better understood. This will lead to progressive application in achieving divergent expectations.

From productive interactions to impact pathways: Understanding the key dimensions in developing SSH research societal impact

Impact is increasingly important for science policy-makers; science policy studies has reacted this heightened urgency by studying these policy-interventions meaning that policy has developed more quickly than theory. This has led to the prevalence of a ‘common sense’ impact definition: research’s societal impact are direct economic effects, such as income generated by licenses, patents and spin-out companies. These indicators are recognised as weak proxies for research’s societal benefits, and in response, science policy has undertaken a huge descriptive effort to more precisely define impact. SSH disciplines have been highly active in this because economic metrics are very poor measures of their societal impact. One interesting theoretical development describing diversity was Spaapen and Van Drooge’s ‘productive interactions’ concept. In this paper we seek to realise the potential that Spaapen & Van Drooge’s productive interactions concept offers, but which we argue has been lost through its operationalisation as ‘counting interactions’. We address the wider conceptual framework for describing SSH pathways to societal impact by paying attention not only to productive interactions but to the changes they mediate. Drawing on a comparative analysis of 60 examples of SSH impact, we develop a typology of SSH pathways to societal impact. We conclude by arguing the usefulness of the extended conceptual framework and propose policy measures to stimulate and support impact processes.

Behavioral economic demand metrics for abuse deterrent and abuse potential quantification

IntroductionBehavioral economics provides a framework for quantifying drug abuse potential that can inform public health risk, clinical treatment, and research. Hypothetical purchase task (HPT) questionnaires may provide a low-cost and sensitive method by which to measure and predict the appeal of pharmaceutical drugs that differ by formulation. However, the validity of this type of analysis must be empirically established by comparing the “essential value” (EV) of different drugs across subgroups. ProceduresThis pilot study used HPT assessments and the Exponential Model of Demand to quantify the EV of opioid medications—specifically, easily tampered formulations versus (vs.) abuse-deterrent formulations—in patients with a history of opioid abuse. Main findingsParticipants had more inelastic demand for opioid pills than for cigarettes and alcohol. Participants with experience manipulating pills (M group) had more inelastic demand for standard pills vs. participants with no manipulation experience (NM group), and the M group had a more elastic demand for the abuse-deterrent opioid pill than for the standard pill. There was no effect of formulation in the NM group and there was no difference in demand elasticity for abuse-deterrent pills between the two groups. There was a positive correlation between the EVs of different drugs, and between some behavioral economic indices and treatment variables. ConclusionsOur results suggest that HPTs may provide a sensitive measure of abuse potential that can distinguish between different formulations in at-risk populations.

Comparison of internal grid topologies of offshore wind farms regarding reliability and economic performance metrics analysis

Despite higher winds, the investment and operation and maintenance costs of offshore wind farms are still high as compared to onshore solutions. This highlights the importance of an increased reliability, because the occurrence of a failure can result in long downtimes due to inaccessibility of the site during harsh weather conditions. This study performs a reliability analysis of the internal grid of an offshore wind farm, comparing different topologies and their economic outcome. The research addresses both a redundancy between each pair of two strings (classical redundancy design) and a redundancy between already redundantly paired sets of strings (newly proposed redundancy design). The results show that the use of a redundancy scheme leads to a positive Net Present Value (NPV) and an interesting Internal Rate of Return (IRR), which can be an appealing investment. Moreover, it is more profitable to simply rate the cables to support normal operation conditions at rated power rather than increasing the cable ratings to support all failure scenarios. Overall, the topology that shows the best economic results combined with a good reliability is the topology with cable ratings based on regular conditions with a redundancy in the middle of the string between each pair of two strings.