Portfolio Costs Research Articles

Towards equitable infrastructure asset management: Scour maintenance strategy for aging bridge systems in flood-prone zones using deep reinforcement learning

Bridges play a critical role in transportation networks; however, they are vulnerable to deterioration, aging, and degradation, especially in the face of climate change and extreme weather events such as floodings. Furthermore, bridges can significantly affect social vulnerability; their damage or destruction can isolate communities, inhibit emergency responses, and disrupt essential services. Maintaining critical bridges in a cost-effective and sustainable manner is crucial to ensure their longevity and protect vulnerable communities. To address the maintenance optimization problem of bridge systems considering the effects of time deterioration, flood degradation, and social vulnerability, this study proposes a deep reinforcement learning algorithm to optimally allocate resources to bridges that are at expected cost of failure due to scour. The algorithm considers the effects of flood degradation with different return periods and is trained using a Markov Decision Process as the environment. The study conducts four flood simulation scenarios using Geographic Information System data. The findings suggest that the deep reinforcement learning algorithm proposes a sequence of repair actions that outperforms the status quo, currently employed by bridge managers. The significance of this study lies in its valuable insights for cities worldwide on how to effectively optimize their limited resources for the maintenance and rehabilitation of critical infrastructure systems to decrease portfolio cost and increase social equity.

Temporal Contrastive and Spatial Enhancement Coarse Grained Network for Weakly Supervised Group Activity Recognition

Group activity recognition (GAR) is an increasingly popular topic in the field of computer vision. Numerous researchers have proposed a range of methods to achieve outstanding recognition performance. However, these methods invariably require fine-grained personal feature extraction and a large network architecture to aggregate individual features or reason person relationships. To mitigate the need for a bloated portfolio of annotations and high training costs, weak supervision has emerged as a promising approach. Under the weak supervision paradigm, only coarse-grained labels are used during network training. Nevertheless, this method poses two key challenges. Firstly, it is limited in its ability to model temporal relationships among individual persons, and secondly, it tends to focus on less relevant information, thereby leading to suboptimal network parameter optimization. Both of these challenges result in erroneous temporal information judgment and training inefficiencies. To address these challenges within the weak supervision paradigm, we propose a novel Temporal Contrastive and Spatial Enhancement Coarse-Grained Network (TCSE-CGN) to solve the GAR problem. TCSE-CGN comprises two simple yet effective streams, namely the Spatial Enhancement Stream and the Temporal Contrastive Stream. After extracting features using only several RGB frames, half of the extracted feature is sent to the Spatial Enhancement Stream for enhancement using an attention mechanism. Consequently, the network automatically learns more representative information. The remaining feature is sent to the Temporal Contrastive Stream, which uses contrastive learning to model temporal relationships among all RGB frame-level features. Specifically, the network is guided to learn the hidden semantic temporal information about inter-frame sequences. Network parameters are optimized using a combination of universe cross-entropy loss and a novel temporal contrastive loss. Comprehensive experiments are conducted on two widely used datasets, namely the Volleyball dataset and the Collective dataset, to demonstrate the effectiveness of TCSE-CGN. Results show that TCSE-CGN performs competitively with other works that require more supervision and a larger architecture.

Universal Chiplet Interconnect Express Г (UCIeГ)

Align Industry around an open platform to enable chiplet based solutions. Enables construction of SoCs that exceed maximum reticle size -Package becomes new System-on-a-Chip (SoC) with same dies (Scale Up) -Reduces time-to-solution (e.g., enables die reuse) -Lowers portfolio cost (product & project) -Enables optimal process technologies -Smaller (better yield) -Reduces IP porting costs -Lowers product SKU cost -Enables a customizable, standard-based product for specific use cases (bespoke solutions) -Scales innovation (manufacturing and process locked IPs)

Optimal multi-energy portfolio towards zero carbon data center buildings in the presence of proactive demand response programs

Multiple geographically dispersed data center buildings have been increasingly positioned in renewable-rich areas due to their high energy consumption. This paper proposes an optimal multi-energy portfolio for zero carbon data center buildings in the presence of proactive demand response programs. In the data center buildings, the complementarities of hydro-solar-wind hybrid renewable energy sources (RESs) are fully exploited via multi-energy conversion and storage devices, which are modeled as 100% renewable energy hubs for zero carbon multi-energy supplies. In order to exploit the hub-internal operational dispatchability and flexibility, a comprehensive energy consumption model of data center buildings is proposed to facilitate the demand response in terms of available serves, while the variable speed pumped storage (VSPS) is modeled to capture its dynamic operational characteristics. The energy‑carbon production, conversion, storage, and consumption are formulated as an energy‑carbon matrix, which are solved via a two-stage method to obtain the optimal portfolio. Case studies on southwest China data center buildings are implemented to demonstrate the effectiveness and superiority of the proposed methodology on cost-effective accommodation of RESs. Simulations results show that the system portfolio cost can be reduced by at most 17.8% with a higher operational flexibility.

How Sampling and Averaging Historical Solar and Wind Data Can Distort Resource Adequacy

Capacity planning models and resource adequacy assessments have often relied on averaging and sampling techniques that disregard important reasonably expected interactions of weather-based resources. We provide a method to capture the economic value of information and reliability risk from using inadequate sample data to design sustainable systems with high renewable generation. Analysis of long run portfolio cost and sources of uncertainty shows as much as a 16% system cost increase with a 38-fold increase in expected unserved energy when average renewable outputs are modeled rather than a 10 year hourly coincident sample, which illustrates the pitfalls of averaging data and ignoring temporal interdependencies. Investment recommendations can significantly differ depending on which years and how many years are included in the analysis. We show that selecting the wrong year can increase system costs by over 4% with a 7-fold increase in expected unserved energy, failing to meet planned reliability and renewable design targets. It is possible for a single year of coincident load-wind-solar data to reasonably approximate system characteristics; however, the best year changes with renewable penetration.

A mean-variance portfolio optimization approach for high-renewable energy hub

This paper proposes a high-renewable portfolio model of energy hub. In this model, geothermal-solar-wind multi-energy complementarities are fully explored based on electrolytic thermo-electrochemical effects of geothermal-to-hydrogen (GTH), which are converted, conditioned, and coupled through energy hub. The proposed high-renewable energy hub portfolio is an intractable optimization problem due to their inherent strong energy couplings and conflicted energy cost/risk. The original problem is thus characterized through the mean-variance approach to explicitly express the risk associated with the forecast uncertainties. The formulated mean-variance portfolio problem is subsequently modeled as a two-stage mixed-integer nonlinear programming (MINLP) stochastic programming to optimally determine appropriate energy generation, conversion, and storage candidates. Numerical studies on a community microgrid are implemented to verify the effectiveness and superiority of the proposed methodology over conventional wind-solar-battery scheme. Simulations results show that the portfolio cost can be reduced by at most 14.9% with a significantly higher operational flexibility.

The economic potential of district heating under climate neutrality: The case of Austria

This paper focuses on the requirement of the Energy Efficiency Directive for assessing the potential of efficient heating and cooling. The paper describes the method for calculating the economic potential of efficient district heating (DH) in Austria under different scenarios, assuming the full decarbonisation of the heating sector by 2050. A spatially explicit approach is used to derive heat demand density maps, heat distribution costs and potential DH areas. The obtained areas are clustered, and depending on their potential spreads, up to four supply portfolios on the installed generation capacities are assessed in each cluster. While this method maintains the level of detail, it significantly reduces the number of required sensitivity analyses. Finally, the least cost portfolios of DH supply in each cluster are identified and compared with the costs of individual heat supply. The economic potential of DH by 2050 is calculated accordingly. The analyses revealed that the DH market share within DH areas is the main driver of DH competitiveness. Depending on the scenario, the economic DH potential varies between 13% and 50% of space and water heating demand in Austria. The extensive application of renewable gases in this sector was not economically feasible under any scenario.

Balancing India’s 2030 Electricity Grid Needs Management of Time Granularity and Uncertainty: Insights from a Parametric Model

With some of the world's most ambitious renewable energy (RE) growth targets, especially when normalized for scale, India aims more than quadrupling wind and solar by 2030. Simultaneously, coal dominates the electricity grid, providing roughly three-quarters of electricity today. We present results from the first of a kind model to handle high uncertainty, which uses parametric analysis instead of stochastic analysis for grid balancing based on economic despatch through 2030, covering 30-minute resolution granularity at a national level. The model assumes a range of growing demand, supply options, prices, and other uncertain inputs. It calculates the lowest cost portfolio across a spectrum of parametric uncertainty. We apply simplifications to handle the intersection of capacity planning with optimized despatch. Our results indicate that very high RE scenarios are cost-effective, even if a measurable fraction would be surplus and thus discarded ("curtailed"). We find that high RE without storage as well as existing slack in coal- and gas-powered capacity are insufficient to meet rising demand on a real-time basis, especially adding time-of-day balancing. Storage technologies prove valuable but remain expensive compared to the 2019 portfolio mix, due to issues of duty cycling like seasonal variability, not merely inherent high capital costs. However, examining alternatives to batteries for future growth finds all solutions for peaking power are even more expensive. For balancing at peak times, a smarter grid that applies demand response may be cost-effective. We also find the need for more sophisticated modelling with higher stochasticity across annual timeframes (especially year on year changes in wind output, rainfall, and demand) along with uncertainty on supply and load profiles (shapes).

The potential of industrial electricity savings to reduce air pollution from coal-fired power generation in China

Coal-intensive power supply systems, along with a fast-growing electricity demand driven by industry has caused serious air pollution and health concerns. These concerns are particularly prominent in countries where electricity use is likewise dominated by industry and heavily dependent on coal-based electricity. A more efficient industry and coal-free electricity systems are the core components of the United Nations 2030 Agenda for Sustainable Development. Previous studies rarely reflect on the impacts of the electricity savings of industrial consumers on the electricity supply sector with respect to future air emission changes, and also neglect the potential benefits of reducing investments in new generation capacity. Here, a comprehensive modeling framework is newly developed to quantify the connections of electricity savings, coal-based electricity systems, air pollutant emissions, and control investments in China, a country exposed to poor air quality. The modeling framework includes 175 energy efficiency technologies (covering multiple industrial sectors) and detailed information of power generation units (thermal efficiency, environmental performance, and lifespan), and allows for unit-by-unit assessment. We find that industrial efficiency improvements can significantly decrease the dependence on coal-fired power generation, particularly the most polluting power fleet. Efficient use of electricity in industry can drive all small high-polluting coal generation units (i.e. units below 300 MW, in total 753 units) to be phased out and effectively curb less efficient coal-fired plants to come online in China. Meanwhile, the air pollutant emissions can be significantly avoided because of the closed coal-fired power units. Developed cost portfolios demonstrate that improving industrial energy efficiency is more cost-effective than installing flue gas controls in coal-fired plants. We further reveal that a sustainable industry could contribute to climate change mitigation even if less remarkable than air quality improvement, while enabling the expansion of intermittent renewable power supply.

Every Cloud has a Silver Lining: On the Relation between Bank-Affiliated Asset Manager Bias and Mutual Fund Fees

Considering the institutional factors of the German mutual fund market, we analyze equity fund holdings of German retail clients who received financial advice between 2005 and 2014 to investigate whether those investors overweight the bank-affiliated asset manager and if so, whether this bank-affiliated asset manager bias leads to higher fees, i. e. Total Expense Ratios. Our analysis clearly indicates the presence of large bank-affiliated asset management biases for clients of all different banking sectors. Thus, German retail clients follow the biased financial advice they receive from their bank. Surprisingly, this bank-affiliated asset manager bias significantly reduces portfolio costs measured via mutual fund fees. Therefore, German banks disproportionately promote products of bank-affiliated asset managers but this biased advice does not lead to higher portfolio costs.

Optimal Portfolio Insurance under Nonlinear Transaction Costs

The minimization of the costs related to portfolio insurance is a very important investment strategy. In this article, by adding the transaction costs to the classical minimum cost portfolio insurance (MCPI) problem, we define and study the MCPI under transaction costs (MCPITC) problem as a nonlinear programming (NLP) problem. In this way, the MCPI problem becomes more realistic. Since such NLP problems are commonly solved by heuristics, we use the Beetle Antennae Search (BAS) algorithm to provide a solution to the MCPITC problem. Numerical experiments and computer simulations in real-world data sets confirm that our approach is an excellent alternative to other evolutionary computation algorithms.

Graphical analysis of CO2 emissions reduction strategies

The emergence of increased numbers of CO2 utilization options to convert CO2 from emissions sources requires decision-making as to which emission sources to choose for CO2 capture and which of the many available utilization technologies or sequestration options to implement. The overarching goal of any strategy is to achieve emissions reductions at low costs. Recent developments have addressed the problem of designing CO2 Integration Networks with minimum costs to achieve low cost emissions reductions, while considering many alternative processing schemes that exist across multiple CO2 emissions sources and alternative utilization and storage sinks. However, the network optimization is complex and provides results in the form of detailed CO2 processing network configurations as opposed to high-level insights into the problem. There is currently no simple graphical analysis method available that can give quick insights into the economics of the problem across all available CO2 sources, utilization and storage options. In this work, a graphical representation is proposed that allows the analysis of CO2 capture, utilization and storage (CCUS) strategies. It allows the quick identification of the lowest cost portfolio for CO2 utilization and storage options for a given set of CO2 emissions sources without the need to resort to detailed and time-consuming network optimization. It also enables the economic analysis of alternative CCUS strategies developed based on other criteria such as environmental merit. The application of the method is illustrated with an example.

A Reinforcement Learning-Based Decision System for Electricity Pricing Plan Selection by Smart Grid End Users

With the development of deregulated retail power markets, it is possible for end users equipped with smart meters and controllers to optimize their consumption cost portfolios by choosing various pricing plans from different retail electricity companies. This article proposes a reinforcement learning-based decision system for assisting the selection of electricity pricing plans, which can minimize the electricity payment and consumption dissatisfaction for individual smart grid end user. The decision problem is modeled as a transition probability-free Markov decision process (MDP) with improved state framework. The proposed problem is solved using a Kernel approximator-integrated batch Q-learning algorithm, where some modifications of sampling and data representation are made to improve the computational and prediction performance. The proposed algorithm can extract the hidden features behind the time-varying pricing plans from a continuous high-dimensional state space. Case studies are based on data from real-world historical pricing plans and the optimal decision policy is learned without a priori information about the market environment. Results of several experiments demonstrate that the proposed decision model can construct a precise predictive policy for individual user, effectively reducing their cost and energy consumption dissatisfaction.

Long Term Energy Storage in Highly Renewable Systems

Increasing penetrations of intermittent renewable energy generation introduce novel balancing and reliability challenges for electricity systems. Mismatches between renewable energy production and electricity demand cause periods of overgeneration and periods of undergeneration. These latter energy deficit periods will result in reliability challenges unless additional balancing solutions are employed. Energy deficits can range from a few hours to days to seasons to years. A least cost energy system will best meet these balancing challenges with diverse investments in energy infrastructure, depending on technology costs, natural resource availability, interconnectedness, and evolving load patterns, including flexible loads. Short-term and long-term storage applications may both be part of this portfolio. This study looks at storage in isolation to show the types of tradeoffs present between one storage resource and another in providing balancing services. The best storage technologies to balance the system depend on the duration of deficit events it is designed to mitigate. This paper compares the economics of different storage technology types in providing the range of short-term to long-term storage applications. The results compare quantities of conceptual storage technologies at different price points and quantities of clean gas generation to serve balancing needs as part of a least cost portfolio. We examine the tradeoffs between gas generation with low capital costs but high variable costs when burning clean fuels, and the alternative conceptual storage technologies, showing why clean gas capacity may play a role in least cost resource portfolios in decarbonized electricity systems.

Анализ международного опыта обращения наличных денег

Nowadays, attention is being paid to studying the structure of money circulation in foreign countries, which is stipulated by changes in cash monetization of the economy and the role of cash and financial operations performed with cash. According to the results based on the national and foreign research of cash circulation, the advantages and disadvantages of using cash in money turnover have been identified. The rating of countries with the least dependence on cash is given. It has been stated that in the developed countries the bank cards are used as an alternative to cash, the sizes of which vary depending on the location and characteristics of consumer lending in them. The problem is being studied: what reasons stimulate the demand for cash? The factors that influence the cash providing the goods and services circulation, other payments of the population 
 of various countries (assessment of motives for holding money, total demand for cash, transactional, cumulative and speculative or portfolio costs have been given). Cash needs planning also plays an important role. In the Bank of Russia cash need planning is provided on the forecast estimates for all face values of coins and banknotes. A review of a sample of cash circulation and factors affecting it in Russia for the period from 1998 to 2018 is presented. The graphs of changes in the money supply and its growth rates, gross domestic product and its growth rates are illustrated. A correlation matrix is built for all variables in the sample (growth rates). The conclusions are drawn about the need in the monetary mechanism for regulating cash circulation to maintain the national independence and statehood in the context of digitalization of the world economy on a global scale.

Optimal Portfolio Trading: Adaptive Multi-period Trade Execution

We propose a new approach to adaptive multi-period trade execution which can be viewed as an extension of Grinold and Kahn (1995) and Almgren and Chriss (1999). Our methodology does not rely on any exogenous switching criteria but instead explicitly includes trading acceleration and deceleration depending on market conditions into equations describing the trading strategy. In other words, we consider adaptive trading strategies that (among other factors) condition on a current price and therefore are stochastic before the price is revealed. The new approach enables us to specify multi-period mean variance style utility function where expectations are taken over all possible realizations of the price process. Consequently while actual trading will still have stochastic nature, we can compute expected portfolio trading costs as well as optimal response to all market conditions on a pre-trade basis.

Cross-Border Interconnectors in South Asia: Market-Oriented Dispatch and Planning

Regional trade in South Asia has progressed well over the last decade to exceed 3 GW in interconnection capacity, connecting India with Bhutan, Bangladesh and Nepal. We present an analysis of the benefits of the next 10.6 GW of interconnection capacity under construction and planning stages across four major corridors connecting five countries, including the proposed HVDC interconnector between India and Sri Lanka. It is important that these interconnectors are assessed not only for long-term benefits as part of a least cost portfolio of investments, but also for short-term, market-based benefits that can be gained from new opportunities for cross border electricity trade (CBET) in India’s power exchanges. The Electricity Planning Model (EPM) is developed for analysis of regional markets. A combination of market price driven short-term dispatch analysis and long-term planning optimization (for 2019-2035) is conducted using EPM. The analysis shows a strong economic case for development of a South Asian Regional Electricity Market (SAREM). Conservative estimates of (discounted) benefits exceed $1 billion accrued over only 10–12 years for each of the incumbent four major corridors. The short-term analysis using historic spot prices in the Indian Energy Exchange (IEX) also reveals a strong case with annual benefits in the range of $100-400+m pa for these corridors. The model is made available to system operators and planners in the region and used for building their capacity to develop further assessment and inform policy dialogues.

Developing new health technologies for neglected diseases: a pipeline portfolio review and cost model.

Background: Funding for neglected disease product development fell from 2009-2015, other than a brief injection of Ebola funding. One impediment to mobilizing resources is a lack of information on product candidates, the estimated costs to move them through the pipeline, and the likelihood of specific launches. This study aimed to help fill these information gaps. Methods: We conducted a pipeline portfolio review to identify current candidates for 35 neglected diseases. Using an adapted version of the Portfolio to Impact financial modelling tool, we estimated the costs to move these candidates through the pipeline over the next decade and the likely launches. Since the current pipeline is unlikely to yield several critical products, we estimated the costs to develop a set of priority "missing" products. Results: We found 685 neglected disease product candidates as of August 31, 2017; 538 candidates met inclusion criteria for input into the model. It would cost about $16.3 billion (range $13.4-19.8B) to move these candidates through the pipeline, with three-quarters of the costs incurred in the first 5 years, resulting in about 128 (89-160) expected product launches. Based on the current pipeline, there would be few launches of complex new chemical entities; launches of highly efficacious HIV, tuberculosis, or malaria vaccines would be unlikely. Estimated additional costs to launch one of each of 18 key missing products are $13.6B assuming lowest product complexity or $21.8B assuming highest complexity ($8.1B-36.6B). Over the next 5 years, total estimated costs to move current candidates through the pipeline and develop these 18 missing products would be around $4.5B (low complexity missing products) or $5.8B/year (high complexity missing products). Conclusions: Since current annual global spending on product development is about $3B, this study suggests the annual funding gap over the next 5 years is at least $1.5-2.8B.

What is the optimal power generation mix of China? An empirical analysis using portfolio theory

This paper employs portfolio theory to explore China’s optimal power generation mix in 2030, which considers the possibilities of technological developments, and the government’s non-fossil generation and non-hydro renewable generation policy targets. The aim of this paper is to investigate China’s efficient generation portfolios by comparing the portfolio costs, risks, efficient frontiers, and diversification levels under different cases and scenarios. The results show that fossil fuel generation technologies have no advantages when considering the technological restrictions. In addition, various preferences for non-fossil generation technologies are influenced greatly by the goals of pursuing cost or risk minimization and different policy targets. Positive effects exist for a non-fossil generation goal or a policy target package from the perspectives of minimizing cost and risk, as well as for improving the diversification level.

Evaluating the reliability of efficient energy technology portfolios

This paper develops a decision evaluation framework to assess how the treatment of risk affects the reliability of, and investment into, electricity generation infrastructure. First, portfolios of electricity generation technologies that comprise the energy supply systems in the US are evaluated using a mean-variance approach. Second, this research assesses the reliability of the portfolios with the aid of loss of load expectation and loss of energy expectation metrics. The methodology considers the least-cost technology mix coupled with the reduction of market and system risks. The variation in the portfolio cost is based on the prevailing policies in the geographic locations. Overall, the current mix of technologies evaluated along the cost-risk latitudes shows an inefficient electricity technology portfolio system. First, investments in renewable technologies may create a bifurcation. On the one hand, the portfolios with significant proportions of the high-cost intermittent technologies exhibit low market risks. On the other hand, these portfolios have less desirable system reliability measures. Second, policy makers will find it instructive that a more diverse electricity technology mix offers the potential to migrate to the efficient frontier in the near term. However, it is imperative to craft policies in support of the transition with the caveat that technology diversity is not always a panacea for improving system reliability even if the portfolio is on the efficient frontier. This work projects some intriguing insights and offers guidance for policy makers.