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1
He, Yuanyuan, Luxin Wan, Manli Zhang, and Huijuan Zhao. "Regional Renewable Energy Installation Optimization Strategies with Renewable Portfolio Standards in China." Sustainability 14, no. 17 (August 23, 2022): 10498. http://dx.doi.org/10.3390/su141710498.
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In this paper, we provide theoretical and policy support for quota-allocation strategies based on a national unified renewable energy (RE) power market. Renewable portfolio standards (RPSs) are of great significance in promoting the stable development of renewable energy and improving power market decision making in China’s power industry. To resolve the geographical, resource allocation, and power-grid problems of multi-regional RE power generation, we constructed a regional distribution optimization model with the lowest cost under the RPS policy and designed a set of dynamic distribution mechanisms based on the renewable energy power quota index. The results show that it is necessary to prioritize development of wind-generated power on the North China and Northeast Power Grids, solar energy on the Northwest Power Grid, and biomass energy generation on grids in other regions to plan specific task undertakings and allocate RE power generation to each grid. We propose a multi-regional power distribution model at the lowest cost under the RPS policy to provide solutions and references for renewable energy power market quota allocation.
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Lystbæk, Nicolai, Mikkel Gregersen, and Hamid Reza Shaker. "Review of Energy Portfolio Optimization in Energy Markets Considering Flexibility of Power-to-X." Sustainability 15, no. 5 (March 1, 2023): 4422. http://dx.doi.org/10.3390/su15054422.
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Power-to-X is one of the most attention-grabbing topics in the energy sector. Researchers are exploring the potential of harnessing power from renewable technologies and converting it into fuels used in various industries and the transportation sector. With the current market and research emphasis on Power-to-X and the accompanying substantial investments, a review of Power-to-X is becoming essential. Optimization will be a crucial aspect of managing an energy portfolio that includes Power-to-X and electrolysis systems, as the electrolyzer can participate in multiple markets. Based on the current literature and published reviews, none of them adequately showcase the state-of-the-art optimization algorithms for energy portfolios focusing on Power-to-X. Therefore, this paper provides an in-depth review of the optimization algorithms applied to energy portfolios with a specific emphasis on Power-to-X, aiming to uncover the current state-of-the-art in the field.
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Yu, Biying, Zihao Zhao, Guangpu Zhao, Runying An, Feihu Sun, Ru Li, and Xiaohan Peng. "Provincial renewable energy dispatch optimization in line with Renewable Portfolio Standard policy in China." Renewable Energy 174 (August 2021): 236–52. http://dx.doi.org/10.1016/j.renene.2021.04.055.
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Xu, Da, Ziyi Bai, Xiaolong Jin, Xiaodong Yang, Shuangyin Chen, and Ming Zhou. "A mean-variance portfolio optimization approach for high-renewable energy hub." Applied Energy 325 (November 2022): 119888. http://dx.doi.org/10.1016/j.apenergy.2022.119888.
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Mamkhezri, Jamal, Leonard A. Malczynski, and Janie M. Chermak. "Assessing the Economic and Environmental Impacts of Alternative Renewable Portfolio Standards: Winners and Losers." Energies 14, no. 11 (June 5, 2021): 3319. http://dx.doi.org/10.3390/en14113319.
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State-mandated renewable portfolio standards affect substantial portions of the total U.S. electricity supply. Renewable portfolio standards are environmentally motivated policies, yet they have the potential to greatly impact economy. There is not an agreement in the literature on the impact of renewable portfolio standards policies on regional economies, especially on job creation. By integrating various methodologies including econometrics, geographic information system, and input–output analysis into a unique system dynamics model, this paper estimates the economic and environmental impacts of various renewable portfolio standards scenarios in the state of New Mexico, located in Southwestern U.S. The state is endowed with traditional fossil fuel resources and substantial renewable energy potential. In this work we estimated and compared the economic and environmental tradeoffs at the county level under three renewable portfolio standards: New Mexico’s original standard of 20% renewables, the recently adopted 100% renewables standard, and a reduced renewable standard of 10%. The final one would be a return to a more traditional generation profile. We found that while the 20% standard has the highest market-based economic impact on the state as a whole, it is not significantly different from other scenarios. However, when environmental impacts are included, the 100% standard yields the highest value. In addition, while the state level economic impacts across the three scenarios are not significantly different, the county-level impacts are substantial. This is especially important for a state like New Mexico, which has a high reliance on energy for economic development. A higher renewable portfolio standard appears to be an economic tool to stimulate targeted areas’ economic growth. These results have policy implications.
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Hu, Jing, Robert Harmsen, Wina Crijns-Graus, and Ernst Worrell. "Geographical optimization of variable renewable energy capacity in China using modern portfolio theory." Applied Energy 253 (November 2019): 113614. http://dx.doi.org/10.1016/j.apenergy.2019.113614.
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Ko, Woong, Jaeho Lee, and Jinho Kim. "The Effect of a Renewable Energy Certificate Incentive on Mitigating Wind Power Fluctuations: A Case Study of Jeju Island." Applied Sciences 9, no. 8 (April 20, 2019): 1647. http://dx.doi.org/10.3390/app9081647.
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As renewable energy penetration in power systems grows, adequate energy policies are needed to support the system’s operations with flexible resources and to adopt more sustainable energies. A peak-biased incentive for energy storage systems (ESS) using the Korean renewable portfolio standard could make power system operations more difficult. For the first time in the research, this study evaluates the effect of imposing a renewable energy certificate incentive in off-peak periods on mitigating wind power fluctuations. We design a coordinated model of a wind farm with an ESS to model the behavior of wind farm operators. Optimization problems are formulated as mixed integer linear programming problems to test the implementation of revenue models under Korean policy. These models are designed to consider additional incentives for discharging the ESS during off-peak periods. The effects of imposing the incentives on wind power fluctuations are evaluated using the magnitude of the renewable energy certificate (REC) multiplier.
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Bhattacharya, Anindya, and Satoshi Kojima. "Power sector investment risk and renewable energy: A Japanese case study using portfolio risk optimization method." Energy Policy 40 (January 2012): 69–80. http://dx.doi.org/10.1016/j.enpol.2010.09.031.
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Fan, Jing-Li, Jia-Xing Wang, Jia-Wei Hu, Yu Wang, and Xian Zhang. "Optimization of China’s provincial renewable energy installation plan for the 13th five-year plan based on renewable portfolio standards." Applied Energy 254 (November 2019): 113757. http://dx.doi.org/10.1016/j.apenergy.2019.113757.
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Tcvetkov, Pavel. "Climate Policy Imbalance in the Energy Sector: Time to Focus on the Value of CO2 Utilization." Energies 14, no. 2 (January 13, 2021): 411. http://dx.doi.org/10.3390/en14020411.
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Global warming is an existential threat to humanity and the rapid energy transition, which is required, will be the defining social, political and technical challenge of the 21st century. Practical experience and research results of recent years have showed that our actions to cover the gap between real situation and aims of climate agreements are not enough and that improvements in climate policy are needed, primarily in the energy sector. It is becoming increasingly clear that hydrocarbon resources, which production volume is increasing annually, will remain a significant part of the global fuel balance in the foreseeable future. Taking this into account, the main problem of the current climate policy is a limited portfolio of technologies, focused on replacement of hydrocarbon resources with renewable energy, without proper attention to an alternative ways of decreasing carbon intensity, such as carbon sequestration options. This study shows the need to review the existing climate policy portfolios through reorientation to CO2 utilization and disposal technologies and in terms of forming an appropriate appreciation for the role of hydrocarbon industries as the basis for the development of CO2-based production chains. In this paper we argue that: (1) focusing climate investments on a limited portfolio of energy technologies may become a trap that keeps us from achieving global emissions goals; (2) accounting for greenhouse gas (GHG) emissions losses, without taking into account the potential social effects of utilization, is a barrier to diversifying climate strategies; (3) with regard to hydrocarbon industries, a transition from destructive to creative measures aimed at implementing environmental projects is needed; (4) there are no cheap climate solutions, but the present cost of reducing CO2 emissions exceeds any estimate of the social cost of carbon.
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Hyde, Graham, and Brian D. Fath. "Ecological Network Analysis of State-Level Energy Consumption in Maryland, USA." Energies 15, no. 16 (August 18, 2022): 5995. http://dx.doi.org/10.3390/en15165995.
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Renewable and clean energy sources are being integrated into the United States’ modern energy industry to mitigate climate change effects, creating a more complex network of energy production, distribution, and consumption. This study defines the state of Maryland’s energy industry as a network of producers and consumers and analyzes the network’s characteristics by using ecological network analysis (ENA), an analytical tool useful for identifying a system’s indirect effects. The energy industry within Maryland is analyzed over a nine-year time span to understand how its evolution is influencing the network’s characteristics. Maryland’s renewable portfolio standard (RPS) for the year 2030 is then simulated by adjusting renewable and non-renewable energy sources according to energy trends and related state policy. Results from the ENA over the nine-year period of 2010–2019 indicate that the energy industry is highly linear. While typical cycling indices range from 5–15% in ecological energy flow models, cycling indices in this study ranged from 0.007% to 0.0082%. Maryland’s energy industry in the year 2030 is simulated and displays increased cycling because renewable sources typically feed the electricity sector for energy distribution, increasing indirect pathways within the system. The percentage of electricity generated by renewable energy increased from 9.71% in 2019 to 50% in 2030, as mandated in the RPS. Network analyses here emphasize the large gap between Maryland’s current energy infrastructure and what is necessary to meet its renewable targets in 2030. Furthermore, they indicate that a more uniform distribution of energy to consumers may increase efficiency in modern energy industries.
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Khan, Kanwal Iqbal, Syed M. Waqar Azeem Naqvi, Muhammad Mudassar Ghafoor, and Rana Shahid Imdad Akash. "Sustainable Portfolio Optimization with Higher-Order Moments of Risk." Sustainability 12, no. 5 (March 5, 2020): 2006. http://dx.doi.org/10.3390/su12052006.
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Sustainable economic growth and development of stock market plays an important role in diversifying the investment opportunities that can be assessed accordingly. However, a true diversification in portfolio is impossible without inclusion of higher-order moments, skewness and kurtosis. However, the risk-taking behavior of investors is modelled with the help of higher-order moments of risk. Therefore, this study is intended to construct optimal portfolios and efficient frontiers with the inclusion of higher-order moments of risk. The findings show that optimized portfolios with inclusion of skewness and kurtosis are sustainable and significantly different than those from mean-variance optimized portfolios which show asymmetric and fat-tail risk. Results further confirm its significance in balancing the additional risk dimensions and returns in Asian emerging stock markets for sustainable returns. The results also endorse that induction of skewness and kurtosis affects portfolio allocation weights and expected returns. Therefore, this study strongly recommends the inclusion of higher moments of risk for optimization to curtail their effect and sub-optimal decisions.
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Xu, Yujie, Vivian Loftness, and Edson Severnini. "Using Machine Learning to Predict Retrofit Effects for a Commercial Building Portfolio." Energies 14, no. 14 (July 19, 2021): 4334. http://dx.doi.org/10.3390/en14144334.
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Buildings account for 40% of the energy consumption and 31% of the CO2 emissions in the United States. Energy retrofits of existing buildings provide an effective means to reduce building consumption and carbon footprints. A key step in retrofit planning is to predict the effect of various potential retrofits on energy consumption. Decision-makers currently look to simulation-based tools for detailed assessments of a large range of retrofit options. However, simulations often require detailed building characteristic inputs, high expertise, and extensive computational power, presenting challenges for considering portfolios of buildings or evaluating large-scale policy proposals. Data-driven methods offer an alternative approach to retrofit analysis that could be more easily applied to portfolio-wide retrofit plans. However, current applications focus heavily on evaluating past retrofits, providing little decision support for future retrofits. This paper uses data from a portfolio of 550 federal buildings and demonstrates a data-driven approach to generalizing the heterogeneous treatment effect of past retrofits to predict future savings potential for assisting retrofit planning. The main findings include the following: (1) There is high variation in the predicted savings across retrofitted buildings, (2) GSALink, a dashboard tool and fault detection system, commissioning, and HVAC investments had the highest average savings among the six actions analyzed; and (3) by targeting high savers, there is a 110–300 billion Btu improvement potential for the portfolio in site energy savings (the equivalent of 12–32% of the portfolio-total site energy consumption).
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Cesarone, Francesco, Manuel Luis Martino, and Alessandra Carleo. "Does ESG Impact Really Enhance Portfolio Profitability?" Sustainability 14, no. 4 (February 11, 2022): 2050. http://dx.doi.org/10.3390/su14042050.
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Over the last few decades, growing attention to the topic of social responsibility has affected financial markets and institutional authorities. Indeed, recent environmental, social, and financial crises have inevitably led regulators and investors to take into account the sustainable investing issue; however, the question of how Environmental, Social, and Governance (ESG) criteria impact financial portfolio performances is still open. In this work, we examine a multi-objective optimization model for portfolio selection, where we add to the classical Mean-Variance analysis a third non-financial goal represented by the ESG scores. The resulting optimization problem, formulated as a convex quadratic programming, consists of minimizing the portfolio variance with parametric lower bounds on the levels of the portfolio expected return and ESG. We provide here an extensive empirical analysis on five datasets involving real-world capital market indexes from major stock markets. Our empirical findings typically reveal the presence of two behavioral patterns for the 16 Mean-Variance-ESG portfolios analyzed. Indeed, over the last fifteen years we can distinguish two non-overlapping time windows on which the inclusion of portfolio ESG targets leads to different regimes in terms of portfolio profitability. Furthermore, on the most recent time window, we observe that, for the US markets, imposing a high ESG target tends to select portfolios that show better financial performances than other strategies, whereas for the European markets the ESG constraint does not seem to improve the portfolio profitability.
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Zhang, Zhiying, Huchang Liao, and Anbin Tang. "Renewable energy portfolio optimization with public participation under uncertainty: A hybrid multi-attribute multi-objective decision-making method." Applied Energy 307 (February 2022): 118267. http://dx.doi.org/10.1016/j.apenergy.2021.118267.
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Liu, Jicheng, and Qiongjie Dai. "Portfolio Optimization of Photovoltaic/Battery Energy Storage/Electric Vehicle Charging Stations with Sustainability Perspective Based on Cumulative Prospect Theory and MOPSO." Sustainability 12, no. 3 (January 29, 2020): 985. http://dx.doi.org/10.3390/su12030985.
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Recently, an increasing number of photovoltaic/battery energy storage/electric vehicle charging stations (PBES) have been established in many cities around the world. This paper proposes a PBES portfolio optimization model with a sustainability perspective. First, various decision-making criteria are identified from perspectives of economy, society, and environment. Secondly, the performance of alternatives with respect to each criterion is evaluated in the form of trapezoidal intuitionistic fuzzy numbers (TrIFN). Thirdly, the alternatives are ranked based on cumulative prospect theory. Then, a multi-objective optimization model is built and solved by multi-objective particle swarm optimization (MOPSO) algorithm to determine the optimal PBES portfolio. Finally, a case in South China is studied and a scenario analysis is conducted to verify the effectiveness of the proposed model.
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Park, Seyoung, Eun Ryung Lee, Sungchul Lee, and Geonwoo Kim. "Dantzig Type Optimization Method with Applications to Portfolio Selection." Sustainability 11, no. 11 (June 10, 2019): 3216. http://dx.doi.org/10.3390/su11113216.
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This paper investigates a novel optimization problem motivated by sparse, sustainable and stable portfolio selection. The existing benchmark portfolio via the Dantzig type optimization is used to construct a sparse, sustainable and stable portfolio. Based on the formulations, this paper proposes two portfolio selection methods, west and north portfolio selection, and investigates their empirical properties. Numerical results presented for 12 datasets and various simulated data show that the west selection can reduce risk, and the north selection may outperform the benchmark as to risk-adjusted returns (based on, e.g., information ratio and Sharpe ratio).
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Pyka, Irena, and Aleksandra Nocoń. "Responsible Lending Policy of Green Investments in the Energy Sector in Poland." Energies 14, no. 21 (November 4, 2021): 7298. http://dx.doi.org/10.3390/en14217298.
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The paper concerns the issue of responsible involvement of commercial banks in Poland in green financing of the energy sector. The main reason for undertaking this topic is the observed increased interest of domestic banks in green financing of investments on the energy market in Poland. Therefore, the main objective are to explore the determinants of changes in the level and structure of bank loans under the influence of green investments in the energy sector in Poland. The article verifies the research hypothesis which assumes that an increase in financing green investments by bank loans in the energy market in Poland requires strengthening the synergy of responsible financing of sustainable development of the economy. For this purpose, a two-stage concept of the empirical research was adopted. On the first stage, questionnaire surveys were conducted among the largest Polish commercial banks to examine the respondents’ acceptance degree of the concept of sustainable financing and greening the loan portfolio. On the second stage, case studies were analyzed along with the analysis of selected secondary quantitative data. It was proven that commercial banks in Poland increase their commitment to sustainable financing, which is observed in the sectorally progressing process of “greening” the credit offer. There is also a noticeable change in their approach to social responsibility, especially in the context of the energy market, where financing of traditional, ecologically harmful projects is still dominant. However, this trend is slowly being reversed, towards supporting investments in the area of modern, environmentally-friendly energy solutions. However, “greening” of loan portfolios in the native banking sector requires a responsible lending policy based on various complex business decisions. Increasing their pro-ecological awareness of financing the economy is only a prerequisite, albeit inadequate, of further energy transformation in Poland.
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Biasin, Massimo, Roy Cerqueti, Emanuela Giacomini, Nicoletta Marinelli, Anna Grazia Quaranta, and Luca Riccetti. "Macro Asset Allocation with Social Impact Investments." Sustainability 11, no. 11 (June 4, 2019): 3140. http://dx.doi.org/10.3390/su11113140.
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Using a unique dataset of 50 listed companies that meet the majority of the OECD requirements for social impact investments, we construct a social impact finance stock index and investigate how investing in social impact firms can contribute to portfolio risk-return performance. We build portfolios with three different methodologies (naïve, Markowitz mean-variance optimization, GARCH-copula model), and we study the performance in terms of returns, Sharpe ratio, utility, and forecast premium based on a constant relative risk aversion function for investors with different levels of risk aversion. Consistent with the idea that social impact investment can improve portfolio risk-return performance, the results of our macro asset allocation analysis show the importance of a large fraction of investor portfolios’ stake committed to social impact investments.
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Osawa, Jun. "Portfolio Analysis of Clean Energy Vehicles in Japan Considering Copper Recycling." Sustainability 15, no. 3 (January 22, 2023): 2113. http://dx.doi.org/10.3390/su15032113.
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Several countries are moving toward carbon neutrality to mitigate climate change. The introduction of clean energy vehicles (CEVs) is a measure to offset the adverse effects of global warming. However, each CEV has its strengths and weaknesses. An optimal CEV portfolio must be formulated to create effective policies that promote innovative technologies and introduce them into the market. CEVs also consume more copper than gasoline vehicles. Copper is associated with supply risks, which most previous conventional studies have failed to address. Therefore, this study proposes a novel CEV optimization model for sustainable consumption of copper resources through recycling along with reduction of CO2 emissions. This study aims to analyze the optimal portfolio for domestic passenger vehicles and the assumed effects of copper recycling and usage reduction. For this analysis, this study set up scenarios for the recycling rate of copper contained in end-of-life vehicles and the reduction rate of copper used in newly sold vehicles. Our simulation results showed that increased recycling rates and reduced use of copper are necessary for the diffusion of battery electric vehicles. Furthermore, the simulation results indicated that if these improvements are not implemented, the deployment of fuel cell vehicles needs to be accelerated.
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Ielasi, Federica, Paolo Ceccherini, and Pietro Zito. "Integrating ESG Analysis into Smart Beta Strategies." Sustainability 12, no. 22 (November 11, 2020): 9351. http://dx.doi.org/10.3390/su12229351.
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Smart beta strategy is an increasingly frequent approach to investment analysis for portfolio selection and optimization and it can be combined with environmental, social, and governance (ESG) considerations. In order to verify the impact of the integration between ESG and smart beta analysis, first we apply a portfolio rebalancing based on ESG scores on securities selected according to different smart beta strategies (ex-post ESG rebalancing approach). Secondly, we apply different smart beta approaches to sustainable portfolios, screened according to the issuers’ ESG scores (ex-ante ESG screening approach). We find that ESG rebalancing and screening are able to impact both on return and risk statistics, but with a different level of efficiency for each smart beta strategy. ESG rebalancing proves to be particularly efficient when it is applied to a “Value” portfolio. On the other hand, when smart beta is applied to ESG-screened portfolios, “Growth” is the strategy which shows the highest increase in risk-adjusted performance, particularly in the US. Minimum volatility proves to be the most efficient smart beta strategy for sustainable portfolios. In general, the increase in the level of sustainability does not deteriorate the risk-adjusted performances of most smart beta strategies.
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Amon, Julian, Margarethe Rammerstorfer, and Karl Weinmayer. "Environmental Portfolios—Evidence from Screening and Passive Portfolio Management." Sustainability 13, no. 22 (November 16, 2021): 12647. http://dx.doi.org/10.3390/su132212647.
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Environmental portfolios via screening or optimization with respect to ecological criteria are not clear-cut concepts. Often, they urge investors to reduce the asset universe, which is accompanied by diversification losses. In this article, we show that a simple passive asset selection strategy based on environmental criteria allows ecological investors to adjust their portfolios without compromising or even reducing risk-adjusted financial performance. In detail, we show that screening does not lead to a significant financial performance reduction. Moreover, we propose an asset selection based on an environmental criteria that improves the portfolios’ financial performance, and further improves its potential positive environmental impact. Our results suggest that a combination of a screening and an environmental-scoring-based asset allocation seems to be a viable option for environmentally responsible investors leveraging the advantages of both strategies. Furthermore, we construct a risk factor CMP (clean minus polluting) and document a significant factor loading when added to the Fama–French five-factor model, suggesting the existence of a risk premium based on a firm’s environmental performance.
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Al-Aboosi and El-Halwagi. "A Stochastic Optimization Approach to the Design of Shale Gas/Oil Wastewater Treatment Systems with Multiple Energy Sources under Uncertainty." Sustainability 11, no. 18 (September 5, 2019): 4865. http://dx.doi.org/10.3390/su11184865.
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The production of shale gas and oil is associated with the generation of substantial amounts of wastewater. With the growing emphasis on sustainable development, the energy sector has been intensifying efforts to manage water resources while diversifying the energy portfolio used in treating wastewater to include fossil and renewable energy. The nexus of water and energy introduces complexity in the optimization of the water management systems. Furthermore, the uncertainty in the data for energy (e.g., solar intensity) and cost (e.g., price fluctuation) introduce additional complexities. The objective of this work is to develop a novel framework for the optimizing wastewater treatment and water-management systems in shale gas production while incorporating fossil and solar energy and accounting for uncertainties. Solar energy is utilized via collection, recovery, storage, and dispatch of heat. Heat integration with an adjacent industrial facility is considered. Additionally, electric power production is intended to supply a reverse osmosis (RO) plant and the local electric grid. The optimization problem is formulated as a multi-scenario mixed integer non-linear programming (MINLP) problem that is a deterministic equivalent of a two-stage stochastic programming model for handling uncertainty in operational conditions through a finite set of scenarios. The results show the capability of the system to address water-energy nexus problems in shale gas production based on the system’s economic and environmental merits. A case study for Eagle Ford Basin in Texas is solved by enabling effective water treatment and energy management strategies to attain the maximum annual profit of the entire system while achieving minimum environmental impact.
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Sanchez-Escobar, Marlene Ofelia, Julieta Noguez, Jose Martin Molina-Espinosa, Rafael Lozano-Espinosa, and Genoveva Vargas-Solar. "The Contribution of Bottom-Up Energy Models to Support Policy Design of Electricity End-Use Efficiency for Residential Buildings and the Residential Sector: A Systematic Review." Energies 14, no. 20 (October 10, 2021): 6466. http://dx.doi.org/10.3390/en14206466.
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Bottom-up energy models are considered essential tools to support policy design of electricity end-use efficiency. However, in the literature, no study analyzes their contribution to support policy design of electricity end-use efficiency, the modeling techniques used to build them, and the policy instruments supported by them. This systematic review fills that gap by identifying the current capability of bottom-up energy models to support specific policy instruments. In the research, we review 192 publications from January 2015 to June 2020 to finally select 20 for further examination. The articles are analyzed quantitatively in terms of techniques, model characteristics, and applied policies. The findings of the study reveal that: (1) bottom-up energy models contribute to the support of policy design of electricity end-use efficiency with the application of specific best practices (2) bottom-up energy models do not provide a portfolio of analytical methods which constraint their capability to support policy design (3) bottom-up energy models for residential buildings have limited policy support and (4) bottom-up energy models’ design reveals a lack of inclusion of key energy efficiency metrics to support decision-making. This study’s findings can help researchers and energy modelers address these limitations and create new models following best practices.
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Gatzert, Nadine, Alexander Martin, Martin Schmidt, Benjamin Seith, and Nikolai Vogl. "Portfolio optimization with irreversible long-term investments in renewable energy under policy risk: A mixed-integer multistage stochastic model and a moving-horizon approach." European Journal of Operational Research 290, no. 2 (April 2021): 734–48. http://dx.doi.org/10.1016/j.ejor.2020.08.033.
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Leng, Kaiqiang, Zhongzhong Li, and Zihao Tong. "How will tradable green certificates affect electricity trading markets under renewable portfolio standards? A China perspective." Clean Energy 6, no. 4 (July 5, 2022): 585–98. http://dx.doi.org/10.1093/ce/zkac038.
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Abstract Renewable portfolio standards (RPS) are important guarantees to promote renewable energy (RE) consumption. The tradable green certificate (TGC) trading mechanism is a supporting mechanism of RPS, but the rate of TGC trading is low and there is a double-metering problem of RE consumption. With the introduction of new policies in China, we innovatively take the electricity-selling side as the subject of RE consumption responsibility and biomass-based electricity-generation (BEG) projects are considered to participate in TGC trading. To explore the interaction between the TGC market and the electricity market, this paper sets up a day-ahead spot market-trading structure combining both markets under RPS and establishes a market equilibrium model. The established model is solved and validated based on the particle swarm optimization algorithm and the profits of each market player under different influencing factors are analysed. The main conclusions are as follows. (i) The established market structure and model effectively solve the double-metering problem of RE consumption, making the TGC turnover rate reach 82.97 %, greatly improving the market efficiency. (ii) Increased demand for TGC will increase demand for RE electricity. The participation of BEG projects in the TGC market can effectively improve the profit of biomass-based electricity producers (BEPs), reduce the burden of government financial subsidies and will not affect the consumption of wind-based electricity and photovoltaic-based electricity. This will help promote the rapid development of China’s RE, especially the BEG industry. (iii) Among the influencing factors, the increase in renewable-energy consumption responsibility weight and the decrease in electricity-generation cost can increase the profit of BEPs. The decline in TGC price and subsidy price will reduce the profit of BEPs. Finally, we put forward policy recommendations for China’s RPS and TGC trading mechanism. This study can provide a reference for the construction of China’s TGC market and electricity market and the development of RE.
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Ye, Yujian, Dawei Qiu, Huiyu Wang, Yi Tang, and Goran Strbac. "Real-Time Autonomous Residential Demand Response Management Based on Twin Delayed Deep Deterministic Policy Gradient Learning." Energies 14, no. 3 (January 20, 2021): 531. http://dx.doi.org/10.3390/en14030531.
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With the roll-out of smart meters and the increasing prevalence of distributed energy resources (DERs) at the residential level, end-users rely on home energy management systems (HEMSs) that can harness real-time data and employ artificial intelligence techniques to optimally manage the operation of different DERs, which are targeted toward minimizing the end-user’s energy bill. In this respect, the performance of the conventional model-based demand response (DR) management approach may deteriorate due to the inaccuracy of the employed DER operating models and the probabilistic modeling of uncertain parameters. To overcome the above drawbacks, this paper develops a novel real-time DR management strategy for a residential household based on the twin delayed deep deterministic policy gradient (TD3) learning approach. This approach is model-free, and thus does not rely on knowledge of the distribution of uncertainties or the operating models and parameters of the DERs. It also enables learning of neural-network-based and fine-grained DR management policies in a multi-dimensional action space by exploiting high-dimensional sensory data that encapsulate the uncertainties associated with the renewable generation, appliances’ operating states, utility prices, and outdoor temperature. The proposed method is applied to the energy management problem for a household with a portfolio of the most prominent types of DERs. Case studies involving a real-world scenario are used to validate the superior performance of the proposed method in reducing the household’s energy costs while coping with the multi-source uncertainties through comprehensive comparisons with the state-of-the-art deep reinforcement learning (DRL) methods.
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Stoilov, Todor, Krasimira Stoilova, and Miroslav Vladimirov. "Explicit Value at Risk Goal Function in Bi-Level Portfolio Problem for Financial Sustainability." Sustainability 13, no. 4 (February 20, 2021): 2315. http://dx.doi.org/10.3390/su13042315.
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The mean-variance (MV) portfolio optimization targets higher return for investment period despite the unknown stochastic behavior of the future asset returns. That is why a risk is explicitly considering, quantified by algebraic characteristics of volatilities and co-variances. A new probabilistic definition of portfolio risk is the Value at Risk (VaR). The paper makes explicit inclusion and minimization of VaR as a quantitative measure of financial sustainability of a portfolio problem. Thus, the portfolio weights as problem solutions will respect not only the MV requirements for risk and return, but also the additional minimization of risk defined by VaR level. The portfolio problem is defined in a new, bi-level form. The upper level minimizes and evaluates the VaR value. The lower level evaluates the optimal assets weights by minimizing portfolio risk and maximizing the return in MV form. The bi-level model allows to have extended set of portfolio solutions with the portfolio weights and the value of VaR. Graphical interpretation of this bi-level definition of the portfolio problem explains the differences with the MV portfolio definition. Thus, the bi-level portfolio problem evaluates the optimal weights, which makes maximization of portfolio return and minimization of the risk in its algebraic and probabilistic form of definition.
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Aslan, Aydin, and Peter N. Posch. "How Do Investors Value Sustainability? A Utility-Based Preference Optimization." Sustainability 14, no. 23 (November 30, 2022): 15963. http://dx.doi.org/10.3390/su142315963.
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We investigate how an investor’s preference for sustainable assets in the portfolio varies for differing levels of risk aversion. Using a sample of 411 publicly listed firms in the S&P 500, we calculate financial and sustainability returns, on which the investor’s utility depends. We approximate the investor’s preference by the exponential and s-shaped utility function and optimize with regard to the sustainability preference. We find that with increasing levels of risk aversion, both minimum-variance and maximum Sharpe ratio type investors seek to incorporate sustainable assets in the portfolio.
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Tamošaitienė, Jolanta, Vahidreza Yousefi, and Hamed Tabasi. "Project Portfolio Construction Using Extreme Value Theory." Sustainability 13, no. 2 (January 16, 2021): 855. http://dx.doi.org/10.3390/su13020855.
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Choosing proper projects has a great impact on organizational success. Firms have various factors for choosing projects based on their different objectives and strategies. The problem of optimization of projects’ risks and returns is among the most prevalent issues in project portfolio selection. In order to optimize and select proper projects, the amount of projects’ expected risks and returns must be evaluated correctly. Determining the relevant distribution is very important in achieving these expectations. In this research, various types of practical distributions were examined, and considering expected and realized risks, the effects of choosing the different distribution on estimation of risks on construction projects were studied.
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Ermolieva, Tatiana, Petr Havlik, Yuri Ermoliev, Nikolay Khabarov, and Michael Obersteiner. "Robust Management of Systemic Risks and Food-Water-Energy-Environmental Security: Two-Stage Strategic-Adaptive GLOBIOM Model." Sustainability 13, no. 2 (January 16, 2021): 857. http://dx.doi.org/10.3390/su13020857.
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Critical imbalances and threshold exceedances can trigger a disruption in a network of interdependent systems. An insignificant-at-first-glance shock can induce systemic risks with cascading catastrophic impacts. Systemic risks challenge traditional risk assessment and management approaches. These risks are shaped by systemic interactions, risk exposures, and decisions of various agents. The paper discusses the need for the two-stage stochastic optimization (STO) approach that enables the design of a robust portfolio of precautionary strategic and operational adaptive decisions that makes the interdependent systems flexible and robust with respect to risks of all kinds. We established a connection between the robust quantile-based non-smooth estimation problem in statistics and the two-stage non-smooth STO problem of robust strategic–adaptive decision-making. The coexistence of complementary strategic and adaptive decisions induces systemic risk aversion in the form of Value-at-Risk (VaR) quantile-based risk constraints. The two-stage robust decision-making is implemented into a large-scale Global Biosphere Management (GLOBIOM) model, showing that robust management of systemic risks can be addressed by solving a system of probabilistic security equations. Selected numerical results emphasize that a robust combination of interdependent strategic and adaptive solutions presents qualitatively new policy recommendations, if compared to a traditional scenario-by-scenario decision-making analysis.
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Chen, Zhiping, Xinkai Zhuang, and Jia Liu. "A Sustainability-Oriented Enhanced Indexation Model with Regime Switching and Cardinality Constraint." Sustainability 11, no. 15 (July 27, 2019): 4055. http://dx.doi.org/10.3390/su11154055.
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Enhanced indexation is an active portfolio management strategy aimed to find a portfolio outperforming a market index. To ensure stable returns and to avoid extreme losses, a sensible enhanced indexation model should be sustainable, where the parameters of the model should be adjusted adaptively according to the market environment. Hence, in this paper, we propose a novel sustainable regime-based cardinality constrained enhanced indexation (RCEI) model, where different benchmarks and cardinalities can be imposed under different market regimes. By using historical observations, the RCEI model is transformed into a deterministic optimization problem with an ℓ 0 norm constraint. We design a partial penalty method coupled with the proximal alternating direction method of multipliers (ADMM) to solve the deterministic optimization problem. Numerical results in UK and US financial markets confirm the superb performance of the sustainability-oriented RCEI model and the efficiency of the algorithm.
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Patel, Martin K., Jean-Sébastien Broc, Haein Cho, Daniel Cabrera, Armin Eberle, Alessandro Federici, Alisa Freyre, et al. "Why We Continue to Need Energy Efficiency Programmes—A Critical Review Based on Experiences in Switzerland and Elsewhere." Energies 14, no. 6 (March 21, 2021): 1742. http://dx.doi.org/10.3390/en14061742.
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Energy efficiency programmes (EEPs) are schemes operated by utilities or other bodies in order to incentivize energy efficiency improvement, in particular by adoption of energy-efficient products and typically by means of an economic reward. Ample experience has been gained, especially in the U.S., where EEPs have been in use for decades, with the rationale of avoiding additional energy supply by improving energy efficiency. More recently, EEPs have been implemented in Europe and in Switzerland. This review paper presents insights from the U.S., the EU and especially from Switzerland, with a focus on levelised programme cost of saved energy (LPC) as a key performance indicator. These LPC values, which take the perspective of the programme operator, are typically low to very low compared to the cost of electricity supply, thereby representing an important argument in favour of their use. The country examples show that EEPs are being effectively and successfully put into practice, for example, in Switzerland both as (i) a national tender-based scheme (called ProKilowatt) and in the form of a (ii) utility-operated obligation-based scheme (in Geneva). EEPs not only call for diligent implementation but also for suitable legal settings, e.g., in the form of mandatory energy efficiency savings targets (as realised for energy efficiency obligations, EEOs) in combination with programme cost recovery. The main criticism of EEPs is the free-rider effect, which needs to be minimised. On the other hand, EEPs are accompanied by significant co-benefits (environmental, health-related and social) and spillover effects. In their currently prevalent form, EEPs allow one to effectively save energy at a (very) low cost (“low-hanging fruit”). They can hence play an important role in fostering the energy transition; however, they should be implemented as part of a policy portfolio, in combination with other policy instruments.
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Masala, Giovanni, Marco Micocci, and Andrea Rizk. "Hedging Wind Power Risk Exposure through Weather Derivatives." Energies 15, no. 4 (February 13, 2022): 1343. http://dx.doi.org/10.3390/en15041343.
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We introduce the industrial portfolio of a wind farm of a hypothetical company and its valuation consistent with the financial market. Next, we propose a static risk management policy originating from hedging against volumetric risk due to drops in wind intensity and we discuss the consequences. The hedging effectiveness firstly requires adequate modeling calibration and an extensive knowledge of these atypical financial (commodity) markets. In this hedging experiment, we find significant benefits for weather-sensitive companies, which can lead to new business opportunities. We provide a new financial econometrics approach to derive weather risk exposure in a typical wind farm. Our results show how accurate risk management can have a real benefit on corporate revenues. Specifically, we apply the spot market price simulation (SMaPS) model for the spot price of electricity. The parameters are calibrated using the prices of the French day-ahead market, and the historical series of the total hourly load is used as the final consumption. Next, we analyze wind speed and its relationship with electricity spot prices. As our main contribution, we demonstrate the effects of a hypothetical hedging strategy with collar options implemented against volumetric risk to satisfy demand at a specific time. Regarding the hedged portfolio, we observe that the “worst value” increases considerably while the earnings-at-risk (EaR) decreases. We consider only volumetric risk management, thus neglecting the market risk associated with electricity price volatility, allowing us to conclude that the hedging operation of our industrial portfolio provides substantial benefits in terms of the worst-case scenario.
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Goldberg, David M., and Sukhwa Hong. "Minimizing the Risks of Highway Transport of Hazardous Materials." Sustainability 11, no. 22 (November 9, 2019): 6300. http://dx.doi.org/10.3390/su11226300.
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Many key industrial and scientific processes, such as the generation of nuclear energy, are of enormous social benefit as energy demand and consumption grow over time. However, a drawback of several such processes is the production of hazardous waste materials, which often requires transportation along highway networks to treatment or disposal facilities. This waste can represent a safety hazard to civilians located along the transportation route. Most prior literature in this domain considers risk within only a single facet, and thus several important risk factors may not be considered. In our paper, we propose a multi-objective program to allow for the analysis and selection of minimally risky routes for hazardous materials transportation. The model assesses risk factors including the length of the selected route, the total population in areas surrounding the selected route, and the likelihood of an accident occurring along the selected route. Our paper uniquely uses geographic information systems (GIS) technology to model this optimization problem. This approach allows us to model risk along multiple dimensions simultaneously. We collect empirical data to test the model and present a case study for risk mitigation using a study area located in California. We show that our multi-objective approach is effective in presenting the decision-maker with a portfolio of solutions that perform well via each factor.
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Song, Chie Hoon. "Examining the Patent Landscape of E-Fuel Technology." Energies 16, no. 5 (February 22, 2023): 2139. http://dx.doi.org/10.3390/en16052139.
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Although the end of combustion engine vehicles seems inevitable under a new climate target for 2030, a complete ban on the combustion engine would be counterproductive. E-fuels, which are produced using renewable electricity from hydrogen and carbon dioxide, could act as a possible large-scale solution for achieving climate-neutral mobility, as they allow us to reduce greenhouse gas emissions while leveraging the existing energy infrastructure. Against such a background, it is critical to examine how the related technological landscape is constructed and might affect the subsequent knowledge generation. By adopting a social-network perspective, the aim of this study is to investigate the degree of technological knowledge relatedness of e-fuel technology using patent data. This is accomplished by analyzing the influence of individual knowledge areas and categorizing them into a matrix model, with each quadrant playing a unique role. The main findings show that the patent landscape is dominated by applications from the private sector, and the main knowledge base is centered around chemical engineering and production techniques for liquid hydrocarbon mixture. Furthermore, the analyzed knowledge flows are dominated by intra-technology knowledge flows, thereby being less prone to convergent technology evolution. In particular, the knowledge areas C10L 01 and C10J 03 demonstrated a high influencer role. The findings can also support R&D advisors and decision makers in policy development in reducing their efforts required for conducting technical intelligence activities and determining adequate policies for R&D portfolio management.
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Yuan, Jun, Jiang Zhu, and Victor Nian. "Neural Network Modeling Based on the Bayesian Method for Evaluating Shipping Mitigation Measures." Sustainability 12, no. 24 (December 15, 2020): 10486. http://dx.doi.org/10.3390/su122410486.
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Climate change caused by greenhouse gas emissions is of critical concern to international shipping. A large portfolio of mitigation measures has been developed to mitigate ship gas emissions by reducing ship energy consumption but is constrained by practical considerations, especially cost. There are difficulties in ranking the priority of mitigation measures, due to the uncertainty of ship information and data gathered from onboard instruments and other sources. In response, a neural network model is proposed to evaluate the cost-effectiveness of mitigation measures based on decarbonization. The neural network is further enhanced with a Bayesian method to consider the uncertainties of model parameters. Three of the key advantages of the proposed approach are (i) its ability to simultaneously consider a wide range of sources of information and data that can help improve the robustness of the modeling results; (ii) the ability to take into account the input uncertainties in ranking and selection; (iii) the ability to include marginal costs in evaluating the cost-effectiveness of mitigation measures to facilitate decision making. In brief, a negative “marginal cost-effectiveness” would indicate a priority consideration for a given mitigation measure. In the case study, it was found that weather routing and draft optimization could have negative marginal cost-effectiveness, signaling the importance of prioritizing these measures.
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Basdekis, Charalampos, Apostolos Christopoulos, Ioannis Katsampoxakis, and Vasileios Nastas. "The Impact of the Ukrainian War on Stock and Energy Markets: A Wavelet Coherence Analysis." Energies 15, no. 21 (November 2, 2022): 8174. http://dx.doi.org/10.3390/en15218174.
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This study attempts to examine the existence of interdependencies between specific stock market indices, exchange rates and crude oil for the period January 2021 to July 2022 with daily data. In the period we have chosen, the post-vaccination phase against COVID-19, as well as the war in Ukraine, is covered. The variables selected for this study are RTSI, Eurostoxx, S&P 500, EUR/USD and RUB/USD exchange rates and crude oil prices. The selection of the specific variables was made because they are directly related to the pre-war period that coincides with the post-vaccine period of the pandemic, which allowed us to characterize it as the normal period and to characterize the period of the war in Ukraine that coincides with the energy crisis as the unstable period. In this way, the present study covers the markets of Russia and other developed economies. For empirical purposes, we applied a wavelet coherence approach in order to investigate the possible existence of simultaneous coherence between the variables at different times and scales for all the considered times. The findings of the study reveal the existence of strong correlations between all variables, during different time periods and for different frequencies during the period under review. Of particular interest is the finding that shows that during the crisis period, the RTSI significantly affects both the European and American stock markets, while also determining the evolution of the Russian currency. In addition, it appears that capital constraints in the Russian stock market, combined with increased demand for crude oil, determine the interdependence between RTSI and crude oil. Finally, an interesting finding of the study is the existence of a negative correlation between the US stock index and crude oil in low-frequency bands and the RTSI and Eurostoxx with crude oil for the post-vaccination and pre-war periods in the medium term. These findings can be used by both investors and portfolio managers to hedge risks and make more confident investment decisions. In addition, these findings can be used by policy makers in the planning of regulatory policies regarding the limitations of the systemic risks in capital markets.
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Park, Jae Ho, Jung-Suk Yu, and Zong Woo Geem. "Optimal Project Planning for Public Rental Housing in South Korea." Sustainability 12, no. 2 (January 14, 2020): 600. http://dx.doi.org/10.3390/su12020600.
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Although Korea has made notable progress in the availability of public rental housing, Korea’s public rental housing representing 6.3% of the country’s total housing is still below the 8% OECD average from 2016. The Seoul Metropolitan Area (composed of Seoul City, Incheon City, and Gyeonggi Province) has nearly 50% of the country’s population, but 11% of the nation’s territory, meaning the area suffers from an acute shortage of public rental housing. This is a serious problem which is hampering the sustainability of Korean society in general. We will examine the possibility of improving this public housing problem using certain algorithms to optimize decision making and resource allocation. This study reviews two pioneering studies on optimal investment portfolio for land development projects and optimal project combination for urban regeneration projects, and then optimizes a public housing investment combination to maximize the amount of public rental houses in Gyeonggi province using optimization techniques. Through the optimal investment combination, public rental houses were found to be more efficiently and sustainably planned for the community.
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Liu, Shu-Shun, and Muhammad Faizal Ardhiansyah Arifin. "Preventive Maintenance Model for National School Buildings in Indonesia Using a Constraint Programming Approach." Sustainability 13, no. 4 (February 9, 2021): 1874. http://dx.doi.org/10.3390/su13041874.
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The Indonesian government needs to maintain around 231,000 school buildings in active use. Such a portfolio of buildings given the diversity of locations, limited maintenance budget, and deterioration rates varied by different building conditions presents many challenges to effective maintenance planning. Many of those schools had been reported to be aging and in a degenerated condition. However, contemporary practice for the planning method of Indonesia’s building maintenance program applies reactive maintenance strategies with a single linear deterioration rate. Such methodology cannot properly guarantee the sustainability of those school buildings. Therefore, this study attempts to examine a different approach to Indonesia’s building maintenance planning by adopting a preventive maintenance strategy using the deterioration rate model proved by historical data from a previous study. This study develops an optimization model with varied deterioration rates and considers the budget limitation, by utilizing a Constraint Programming (CP) approach. The proposed model achieves the minimum maintenance cost for a real case of 41 school buildings under different deterioration rates to ensure adequate building conditions and maintain expected levels of service. Finally, research analysis also proves that this new preventive maintenance model has potential to deliver superior capability for assisting building maintenance decisions in Indonesia’s government.
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Sheng, Jiliang, Juchao Li, and Jun Yang. "Tail Dependency and Risk Spillover between Oil Market and Chinese Sectoral Stock Markets—An Assessment of the 2013 Refined Oil Pricing Reform." Energies 15, no. 16 (August 21, 2022): 6070. http://dx.doi.org/10.3390/en15166070.
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The Chinese refined oil pricing reform in 2013 has brought its refined oil price to be more aligned with the international oil price, helping to mitigate prior distorted pricing mechanisms. Its impact on the correlation, tail risks, and spillover effects between the international crude oil market and Chinese sectoral stock markets warrants empirical assessments. Time-varying copula models and conditional VaR (CoVaR) are employed to examine the correlation between the international oil market and Chinese sectoral stock indexes before and after the 2013 pricing reform, as well as the tail risk and spillover effects of the extreme and moderate oil markets. The results show that: (1) the correlation between the oil market and all 11 Chinese stock sectors is positive both before and after the reform, but the correlation is weaker after the reform than before; (2) The downside tail risk of the extreme and moderate oil markets to most Chinese stock market sectors, and the upside tail risk of the moderate oil market to most stock sectors are lower after the reform; (3) Tail risk spillover effects of extreme oil market on all sectors exist before and after the reform; (4) The upside tail risk spillover effects of moderate oil market exist in most sectors before the reform, but they almost all disappear after the reform. The downside risk spillover effects of the moderate oil market do not exist before or after the reform. The findings provide valuable references for portfolio management and future policy update.
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Gutierrez-Franco, Edgar, Christopher Mejia-Argueta, and Luis Rabelo. "Data-Driven Methodology to Support Long-Lasting Logistics and Decision Making for Urban Last-Mile Operations." Sustainability 13, no. 11 (June 1, 2021): 6230. http://dx.doi.org/10.3390/su13116230.
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Last-mile operations in forward and reverse logistics are responsible for a large part of the costs, emissions, and times in supply chains. These operations have increased due to the growth of electronic commerce and direct-to-consumer strategies. We propose a novel data- and model-driven framework to support decision making for urban distribution. The methodology is composed of diverse, hybrid, and complementary techniques integrated by a decision support system. This approach focuses on key elements of megacities such as socio-demographic diversity, portfolio mix, logistics fragmentation, high congestion factors, and dense commercial areas. The methodological framework will allow decision makers to create early warning systems and, with the implementation of optimization, machine learning, and simulation models together, make the best utilization of resources. The advantages of the system include flexibility in decision making, social welfare, increased productivity, and reductions in cost and environmental impacts. A real-world illustrative example is presented under conditions in one of the most congested cities: the megacity of Bogota, Colombia. Data come from a retail organization operating in the city. A network of stakeholders is analyzed to understand the complex urban distribution. The execution of the methodology was capable of solving a complex problem reducing the number of vehicles utilized, increasing the resource capacity utilization, and reducing the cost of operations of the fleet, meeting all constraints. These constraints included the window of operations and accomplishing the total number of deliveries. Furthermore, the methodology could accomplish the learning function using deep reinforcement learning in reasonable computational times. This preliminary analysis shows the potential benefits, especially in understudied metropolitan areas from emerging markets, supporting a more effective delivery process, and encouraging proactive, dynamic decision making during the execution stage.
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Kroschel, Josh, Minou Rabiei, and Vamegh Rasouli. "Accounting for Fixed Effects in Re-Fracturing Using Dynamic Multivariate Regression." Energies 15, no. 15 (July 27, 2022): 5451. http://dx.doi.org/10.3390/en15155451.
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The oil and gas (O&G) industry is now as focused on minimizing costs and maximizing efficiency just as much as maximizing production. Operators are looking for new and cost-effective ways to add profitable assets to their portfolio. One such way is to re-fracture existing wells. There is evidence that these wells can be very productive in the Bakken. However, because of factors such as depletion and aging wellbore material, re-fracturing wells can be a difficult process to implement successfully and often have binding constraints on surface treating pressure (STP). This study attempts to quantify the effects that completion parameters have on re-fracturing treatment implementation by constructing dynamic fixed effects (FE) multivariate regression models. These models are not generally used in O&G and are more commonly used in economics and policy analysis. However, given that both economics and O&G deal with large amounts of uncertainty for each individual person and well, respectively, these models provide a much simpler approach to handle the uncertainty. These models also attempt to account for stress shadow effects from subsequent stages on treatment. The FE model has the advantage of treating a compilation of well treatment data as panel data and differencing out any unobservable fixed parameters. To the authors’ knowledge, this is the first study using dynamic FE models to estimate temporal stress shadow effects from one stage to the next. These models may then be thought of as estimating the boundary effects from stress shadows, which will affect treatment implementation. The novelty lies in estimating these effects, while accounting for fixed within-well variation, using simpler models than those usually found in industry. We stress that the simplicity of these models is a feature, not a bug. This study found that previous stage average STP, acid volume pumped, and perforation standoff were all statistically significant predictors of average STP with a strong temporal dependence of average STP on subsequent stages after accounting for fixed wellbore and geologic parameters. The models in this study also predict a positive marginal effect from acid volume average STP, which may seem counterintuitive, but is also backed by a previous study.
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Diaz, Rafael, Joshua G. Behr, Rafael Landaeta, Francesco Longo, and Letizia Nicoletti. "Modeling Energy Portfolio Scoring." International Journal of Business Analytics 2, no. 4 (October 2015): 1–22. http://dx.doi.org/10.4018/ijban.2015100101.
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U.S. regions are expected to follow the national trend towards investment in renewable energy as part of their electricity portfolio. The progress of energy portfolios that typically involves traditional methods, such as centralized nuclear and coal-fired generation, and towards cleaner- and renewable-source generation will impact economic growth and public health. Renewable electricity production must strike a balance among cost, reliability, and compatibility. The economic and health benefits obtained from developing an efficient energy portfolio make renewable energy alternatives an important consideration for regions endowed with natural resources. A portfolio mix of production method that considers the economic benefits while limiting adverse health and environmental impacts is attractive. This research proposes a System Dynamics simulation framework to support policy-making efforts in assessing the impact of energy portfolios. The authors demonstrate the utility of the framework by means of analyzing data that pertain to the U.S. Hampton Roads - Peninsula Region.
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Zhou, Jia, Hany Abdel-Khalik, Paul Talbot, and Cristian Rabiti. "A Hybrid Energy System Workflow for Energy Portfolio Optimization." Energies 14, no. 15 (July 21, 2021): 4392. http://dx.doi.org/10.3390/en14154392.
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This manuscript develops a workflow, driven by data analytics algorithms, to support the optimization of the economic performance of an Integrated Energy System. The goal is to determine the optimum mix of capacities from a set of different energy producers (e.g., nuclear, gas, wind and solar). A stochastic-based optimizer is employed, based on Gaussian Process Modeling, which requires numerous samples for its training. Each sample represents a time series describing the demand, load, or other operational and economic profiles for various types of energy producers. These samples are synthetically generated using a reduced order modeling algorithm that reads a limited set of historical data, such as demand and load data from past years. Numerous data analysis methods are employed to construct the reduced order models, including, for example, the Auto Regressive Moving Average, Fourier series decomposition, and the peak detection algorithm. All these algorithms are designed to detrend the data and extract features that can be employed to generate synthetic time histories that preserve the statistical properties of the original limited historical data. The optimization cost function is based on an economic model that assesses the effective cost of energy based on two figures of merit: the specific cash flow stream for each energy producer and the total Net Present Value. An initial guess for the optimal capacities is obtained using the screening curve method. The results of the Gaussian Process model-based optimization are assessed using an exhaustive Monte Carlo search, with the results indicating reasonable optimization results. The workflow has been implemented inside the Idaho National Laboratory’s Risk Analysis and Virtual Environment (RAVEN) framework. The main contribution of this study addresses several challenges in the current optimization methods of the energy portfolios in IES: First, the feasibility of generating the synthetic time series of the periodic peak data; Second, the computational burden of the conventional stochastic optimization of the energy portfolio, associated with the need for repeated executions of system models; Third, the inadequacies of previous studies in terms of the comparisons of the impact of the economic parameters. The proposed workflow can provide a scientifically defendable strategy to support decision-making in the electricity market and to help energy distributors develop a better understanding of the performance of integrated energy systems.
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Zhu, Lei, and Ying Fan. "Optimization of China's generating portfolio and policy implications based on portfolio theory." Energy 35, no. 3 (March 2010): 1391–402. http://dx.doi.org/10.1016/j.energy.2009.11.024.
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Berry, Michael J., Frank N. Laird, and Christoph H. Stefes. "Driving energy: the enactment and ambitiousness of state renewable energy policy." Journal of Public Policy 35, no. 2 (February 9, 2015): 297–328. http://dx.doi.org/10.1017/s0143814x15000045.
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AbstractU.S. states have led the federal government in instituting policies aimed at promoting renewable energy. Nearly all research on renewable portfolio standards (RPSs) has treated RPS adoption as a binary choice. Given the substantial variation in the renewable energy goals established by RPSs, we propose a new measure of RPS ambition that accounts for the amount of additional renewable energy production needed to reach the RPS goal and the number of years allotted to reach the standard. By measuring RPS policy with more precision, our analysis demonstrates that many factors found to affect whether a state will adopt an RPS do not exert a similar effect on the policy’s ambitiousness. Most notably, our analysis demonstrates that Democratic control of the state legislature is the most consequential factor in determining the ambitiousness of state RPS policies.
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Huang, Bi Bin, and Jing Hu. "Renewable Energy Quota System in Italy and its Enlightenment for China." Applied Mechanics and Materials 448-453 (October 2013): 4256–61. http://dx.doi.org/10.4028/www.scientific.net/amm.448-453.4256.
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Renewable energy quota system (also known as renewable portfolio standard) is a new policy to promote development of renewable energy in the world. The typical pattern of the foreign quota system is studied deeply in this paper at first. Based on this, take Italian quota system policy for example, the Italian electricity system overview and key elements of the Italian quota system policies are analyzed. At last, some useful enlightenment gained from renewable portfolio standard in Italy is given to our country.
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Dong, Fugui, Lei Shi, Xiaohui Ding, Yuan Li, and Yongpeng Shi. "Study on China’s Renewable Energy Policy Reform and Improved Design of Renewable Portfolio Standard." Energies 12, no. 11 (June 4, 2019): 2147. http://dx.doi.org/10.3390/en12112147.
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China officially implemented the renewable portfolio standard (RPS) on 1 January 2019, and it remains uncertain as to whether this can effectively solve the problem of renewable energy consumption in China and ease the pressure of government subsidies. In order to study the impact of this policy on China’s renewable energy power generation and explore RPS policy that is more suitable for the characteristics of China’s renewable energy, we first develop a revenue function model based on the just released RPS policy to explore the effectiveness of the policy, the feasibility conditions for successful implementation, and the problems that may be encountered during the implementation process. Then, we propose policy recommendations based on the possible problems of the current policy and design an “incremental electricity price” supplementary policy to improve the possibility of successful implementation of the RPS policy. Finally, an evolutionary game model is established to simulate and verify the possibility of successful implementation of the supplementary policy. The main research results are: (1) the essence of the current RPS policy is the comprehensive implementation policy of the RPS and feed-in-tariff (FiT); (2) because of the characteristics of China’s energy structure, the implementation of this policy reform is more resistant; (3) the quantitative research on the revenue function model shows that the current transaction price of the green certificate market is very low, which is not conducive to alleviating the state’s subsidy pressure on renewable energy power generation; and (4) analysis of empirical data shows that the successful implementation of the “incremental electricity price” policy relies on the initial strategies of grid companies and users.
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Yi, Tao, Yifan Zhang, and Yanfeng Guo. "The Calculation and Optimization Research of Renewable Energy Investment Efficiency under Uncertain Conditions." Open Electrical & Electronic Engineering Journal 12, no. 1 (August 31, 2018): 52–62. http://dx.doi.org/10.2174/1874129001812010052.
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Background: In the renewable energy investment market, there are risks such as fossil fuel price fluctuations, environmental risks caused by pollutant emissions, electricity price fluctuations caused by energy policies, and so on, which bring certain difficulties to measure the investment efficiency. Methods: In this regard, the paper applies the portfolio theory to the Data Envelopment Analysis (DEA) model to evaluate investment efficiency. First of all, the Monte Carlo method is used to simulate the four uncertain factors of fuel unit price, feed-in tariff, annual operating hours, and carbon price, so as to quantitatively measure the risk and return of different power generation. According to the portfolio theory, it evaluates the portfolio risks and returns, respectively as input and output indicators, so as to build a Data Envelopment Analysis (DEA) model to estimate investment efficiency. Conclusion: The simulation and experimental results demonstrate the effectiveness of the presented method. In details, we select a poor efficiency sample, and then, we propose an optimization measure to improve the efficiency. By adjusting the proportion of its investment, the result proves that increasing the proportion of renewable energy can realize optimization and validity of renewable energy investment. Thus, it provides auxiliary support for the investment decision of renewable energy and realizes the coordinated allocation and efficient utilization of renewable energy.