Optimal Investment Plan Research Articles

Optimizing emissions reduction investments in livestock-producing farms under non-linear holding cost and power demand: A comparative analysis of carbon tax and cap-and-trade environmental regulations

Investment in reducing emissions for many diverse companies is a practical strategy to achieve the emissions objectives as outlined by the United Nations Framework Convention on Climate Change. Livestock-producing farms often fall short of sustainability goals, even though many businesses have already adopted an investment strategy towards sustainability with success. Thereby, livestock-producing farms come under increasing responsibility to reduce their environmental impact, and it is now crucial to make simultaneous decision-making of inventory replenishment and emissions reduction investment to ensure the livestock farming industry's sustainability. In this study, a new path towards sustainable development is designed for livestock-producing farms by investigating the optimal investment plan for controlling emissions under carbon tax (CT) and cap-and-trade (C&T) environmental emissions guidelines. Taking into account both edible and non-edible parts of slaughtering mature growing items (GIs), as well as a non-linear holding cost structure and a power demand pattern for edible components, a comprehensive analytical approach implementing mathematical modeling methodology, economic evaluation, and carbon accounting approaches is accomplished. The most economical investment level that not only improves the environment but also adds to the farm's financial stability is identified by examining the interplay between the investments in emissions reduction and replenishment carried out by the farm. The numerical studies demonstrate that implementing an emission reduction strategy under both CT and C&T guidelines results in a 2.45% reduction in carbon emissions for a tax rate of $4.5. In order to further highlight the impacts of investment accessibility and emission parameters, several outcomes of a numerical analysis are reported. The findings demonstrate that implementation of investment strategy is always beneficial in achieving sustainability goals of the farm by lowering emissions under both CT and C&T emissions regulations.

Optimal Investment Strategy Analysis of On-Site Hydrogen Production Based on the Hydrogen Demand Prediction Using Machine Learning

In order to achieve the hydrogen economy and respond to initial hydrogen demand appropriately, a hydrogen production and operation methodology is required to secure the economic feasibility of long-term on-site HRS. This study proposes a novel investment strategy for on-site hydrogen production to meet future hydrogen demand. The optimal investment strategy based on the dual-modal mode of combined autothermal reforming (ATR) and steam methane reforming (SMR) is proposed for hydrogen production using natural gas (NG) as a raw material. To predict hydrogen demand from 2020 to 2030, the machine learning (ML) technique was adopted. R2 and MSE as result using ML were 0.9936 and 6.88×10−5, respectively. In addition, the ATR-SMR hydrogen strategy (ASHS) process was analyzed and compared with the SMR-SMR and ATR-ATR hydrogen strategy (SSHS and AAHS) processes in terms of optimal operation rate, storage tank management, economics, and environmental impacts. The operation rate of proposed hydrogen production processes was determined by the hydrogen demand and storage tank level, and the optimal investment plan to install additional hydrogen process depends on the total amount of hydrogen production. In this study, these results were observed due to the effective combination of the strengths of ATR and SMR. Consequently, the ASHS had the best cost-effectiveness (LCOH at $5.63/kg H2) and environmental friendliness (unit CO2eq emissions at 10.21 kg CO2eq/kg H2 and 1.73 kg CO2eq/kg H2 with CCS). This study includes sensitivity analysis and a comparison of CO2 taxes by the country for three proposed hydrogen production processes. It could contribute to the optimal operation of the on-site hydrogen production system in preparation for future hydrogen demand.

Distribution network tariff design: Facilitate flexible resource under uncertain future energy scenarios

The uncertainty embedded in low-carbon transition pathways and future energy scenarios will reshape the way of distribution network planning and operation. Flexible solutions provided by flexible resources could help distribution network operators (DNOs) to reduce network costs and risks under uncertainties. However, the current tariff paradigm neglects such contributions, which cannot generate sufficient incentives to facilitate flexible resource development. To tackle this issue, this paper first introduces an improved price control method that caps DNOs' revenue while encouraging DNOs' sustainable investment under uncertainties. A bi-level planning model is then proposed to determine the optimal investment plan and evaluate the risk reduction effect by scheduling flexible loads. Finally, a novel pricing method is proposed to derive network tariffs for existing and potential flexible loads based on their contributions to risk reduction and demonstrated in a test system. The result shows a 24% network cost reduction given by 6% DNO risk compensation rate, and 0%–43% and 1%–22% network tariff reductions for existing and future flexible demands, respectively. This evidence proves the proposed method can incentivize DNOs to well manage risks, meanwhile promote the connection and engagement of flexible loads. Regulators and DNOs are recommended to review the existing pricing framework, enhance risk management in price regulation, and explore suitable tariff designs or business models for flexible loads to cope with future uncertainties through flexibility.

Optimal Design of Rotor Sails Based on Environmental Conditions and Cost

Rotor sails are one of the promising solutions to reducing the energy consumption and CO2 emissions of ships. Previous studies focused on how rotor sails affect ship dynamics and energy consumption. In the present study, an optimization-based workflow was proposed to find the optimal design of a rotor sail based on given environmental conditions for a target ship. Since the performance of a rotor sail depends on both operational conditions and the design of the rotor sail, a two-level optimization problem was proposed to separate the optimization of operational conditions and rotor sail design. At the operational level, the spin ratio of a given rotor sail was optimized at each time step under different environmental conditions. Then, the design of the rotor sail was optimized on the design level considering the initial cost of rotor sails and the average operational cost of the ship depending on the environmental conditions and their probabilities. The reductions in energy consumption of ships having optimal rotor sail designs, considering 5-year, 10-year, 15-year, and 20-year investment plans were found to be 0.34%, 2.7%, 3.91%, and 4.29%, respectively. When more severe environmental conditions were assumed for the 10-year investment plan, the diameter of the rotor sail increased and the reduction in energy consumption increased from 2.7% to 4.06%.

A Novel Hybrid Power-Grid Investment Optimization Model with Collaborative Consideration of Risk and Benefit

Power-grid investment (PGI) optimization is crucial for boosting investment performance, lowering investment risks, and assuring the sustainable development of power-grid businesses. However, existing studies, which primarily concentrate on financial aspects, have not adequately considered the risk and benefit factors in the process of PGI. In this context, this research suggests a novel hybrid PGI optimization model that collaboratively accounts for the risks and benefits. In the first step, risk and benefit indicator systems for PGI are built, and a comprehensive evaluation model based on the Bayesian best–worst method and TOPSIS is suggested. In the second stage, a PGI optimization model considering the investment amount, power demand, and low-carbon restrictions is further developed based on the evaluation results. Furthermore, the incomprehensible but intelligible-in-time logic algorithm is adopted to solve the problem. By conducting an empirical analysis of ten projects within a power-grid company, the optimal investment plan and a differentiated investment portfolio strategy are obtained by adjusting the key elements.

BIPV Generation Efficiency Optimal Scheme—— Take Jiangning District of Nanjing City for Example

Building Integrated photovoltaic (BIPV) is more and more applied to the construction field with its advantages of being close to the load center, saving urban land and building materials. Especially under the current goal of 'carbon peak' and 'carbon neutrality', the integration of photovoltaic buildings is one of the important ways to achieve the effective application of renewable energy in buildings. But at present, the lack of relevant application cases of the technology, many owners interested in BIPV have remained on the sidelines. This paper mainly focuses on the economic and environmental benefit evaluation of the roof and curtain wall system of BIPV. By comparing the economic benefits and investment payback period, the owners provide the optimal investment plan, and analyzes the significance of 'two-carbon policy' by calculating the environmental benefits of BIPV. In this paper, Jiangning District of Nanjing city is taken as the pilot object to analyze the benefit of BIPV, and proposed the optimal design strategy of building photovoltaic integration suitable for this area through the benefit comparison of different schemes. In the roof and curtain wall benefit evaluation when we use the photovoltaic garden analysis site for solar radiation estimates, and then get the whole life cycle of photovoltaic system capacity, as the basis of economic benefit evaluation, then through the query related data to save the total coal as the basis of environmental benefit evaluation, combined with the project investment payback period, economic benefits and environmental benefits of comprehensive evaluation of the final plan. Through the above economic benefits and environmental benefits data comparison, photovoltaic roof has more investment value than photovoltaic curtain wall in both economic and environmental aspects. Taking Jiangning District of Nanjing city as an example, this paper illustrates the broad prospect of BIPV, and also provides a relatively clear data support and case reference for the promotion of BIPV nationwide in the future.

Grid integration feasibility and investment planning of offshore wind power under carbon-neutral transition in China

Offshore wind power, with accelerated declining levelized costs, is emerging as a critical building-block to fully decarbonize the world’s largest CO2 emitter, China. However, system integration barriers as well as system balancing costs have not been quantified yet. Here we develop a bottom-up model to test the grid accommodation capabilities and design the optimal investment plans for offshore wind power considering resource distributions, hourly power system simulations, and transmission/storage/hydrogen investments. Results indicate that grid integration barriers exist currently at the provincial level. For 2030, optimized offshore wind investment levels should be doubled compared with current government plans, and provincial allocations should be significantly improved considering both resource quality and grid conditions. For 2050, offshore wind capacity in China could reach as high as 1500 GW, prompting a paradigm shift in national transmission structure, favoring long-term storage in the energy portfolio, enabling green hydrogen production in coastal demand centers, resulting in the world’s largest wind power market.

Hybrid renewable energy powered reverse osmosis desalination – Minimization and comprehensive analysis of levelized cost of water

Supplying desalination plants from renewable resources is a promising solution to overcome water scarcity. Currently, these alternative sources of energy have attracted more attention due to the intensified energy crisis. Since having relatively higher costs, defining an optimal investment plan ensures the cost-effectiveness of the whole system. In previous studies, the desalination unit's size was often considered constant, and power supply components were optimized by minimizing Net Present Cost. An optimized system based on this cost term does not reflect the actual cost of produced water. This paper presents a new configuration and linear formulation for optimal renewable energy-powered water desalination planning. In the proposed model, the size of both the desalination unit and the supply system components are variable and optimized simultaneously by minimizing the Levelized Cost of Water. The water and energy storage are also considered and optimized while emitted carbon by the system is controlled. Besides, a comprehensive sensitivity analysis is performed by changing system configuration, renewable potential, yearly emission cap, desalination unit parameters, and supply system per unit costs. Besides constituting a reliable optimization framework, the study results detect the most influential factors and reveal a future roadmap to further water cost analysis and reduction.

Application of hierarchical clustering on electricity demand of electric vehicles for GEP problems

Increasing fossil fuel consumption and consequently the effects of greenhouse gases (GHGs) on the environment and economy are a major concern for all nations and governments. Electric vehicles (EVs) with plug-in capabilities have the potential to ease such problems. However, the extracted power from the grid for charging the EVs' batteries will significantly impact daily power demand. To satisfy the increasing demand and ensure generation capacity adequacy, the generation expansion planning (GEP) problem is solved to determine the investment decisions for electricity generation sources. Even though there are no centralized utilities for generation planning in most markets, there is still a need to realistically solve the GEP problems and find the optimal investment decisions to tailor the incentives used by most governments to guide the market. There is also a need for a tool to analyze the effect of different charging power levels, charging policies, and penetration levels. The main goal of this paper is to provide a tool to determine realistic optimal investment plans and evaluate different cases. It is also very important to consider the stochastic nature of the electricity demand in GEP problems. We propose a scenario-based stochastic programming model to incorporate the variability in the electricity demand due to EV charging through a set of scenarios generated by Monte Carlo Simulation. The methodology starts with applying a simulation method to generate the electricity demand of EVs by considering all the possible factors affecting EVs' demand. Each iteration of this simulation represents a possible demand profile as a result of penetrating the EVs into the market. Using all these demand profiles in GEP is preferable, but it is not computationally efficient. Computational tractability is achieved by using the clustering technique to reduce the size of such scenarios. We propose clustering methods to select a representative set from the data sets generated by the simulation and integrate EVs into GEP problems by using the selected set. The GEP models are defined to represent EVs' demand explicitly and then solved to imply the benefit of the suggested methods. The results show that GEP models with a representative set produce more realistic solutions than the GEP models including only average EVs demand. To select representative sets, different clustering techniques and distance measurements are used and compared with respect to their performances. Two different methods are defined to choose the best number of clusters: the silhouette coefficient method and the

Optimal Investment Planning of Bankable Multi-Carrier Microgrid Networks

Multi-carrier microgrids are increasingly recognized to play a critical role in capturing the synergies between various vectors of energy, including electricity, gas, and heating/cooling systems, thereby providing an effective platform for handling the complexities associated with the dynamic interactions of the system components. While a recent, growing body of the literature has conclusively shown the effectiveness of multi-carrier microgrids in improving the business cases of localized energy solutions with high penetrations of renewables, the potential benefits associated with interconnecting neighboring multi-carrier microgrids to form networked systems are less well-explored. This is particularly important as networked multi-carrier microgrids capable of trading energy with each other during grid outages can significantly improve the resilience of the aggregated system, whilst at the same time reducing the total discounted costs. In response, this paper introduces an integrated planning framework, formulated as a mixed-integer linear programming problem, for the optimal planning of neighboring multi-carrier microgrids in the presence of demand-side flexibility resources with the aim of minimizing the total discounted cost of the networked microgrids. Based on the numerical evidence obtained from the application of the proposed method to a test-case system, the integrated planning optimization framework's effectiveness in improving the economics and resilience of a cluster of electrically coupled multi-carrier microgrids is validated. More specifically, the numerical simulations confirm the proposed model's effectiveness in a significant reduction of the under-utilized storage and dispatchable generation capacities, as well as the otherwise-curtailed non-dispatchable generations. Accordingly, the findings from this study have important implications for accelerating the transition to locally more reliable, affordable, autonomous microgrids.

Research on Quantitative Trading Investment Strategy Based on LSTM and Dynamic Programming

As financial markets matured, more standardized and quantitative research emerged. In this paper, we model the historical price returns of U.S. gold and BTC over a five-year trading period from September 11, 2016, to September 10, 2021, and estimate the total investment returns as of September 10, 2021. First, we build a variety of price-prediction models to fit the image to the data to get the most accurate image of the actual price trend. It was found that the LSTM neural network-based multi-interval segmentation price prediction model, which has the best prediction effect by comparison with the actual value. Therefore, it was used as the core prediction model for this paper. Then, we use the planning model to find the optimal investment plan by using the difference between the price change of gold and bitcoin trading in the next three days as the decision variable, the highest Sharpe ratio on the third day as the objective function. At the same time, the variance of the forecast value in the next three days is used as the risk characterization, and different weights are assigned to the risk and return as the objective function to characterize the investment strategy under different investment personalities. In order to accelerate the convergence of the planning model, we added a particle swarm algorithm for optimization. In the end, we obtained the results for the specific daily investment scenarios for aggressive investors, intermediate investors, and steady investors.

Trading Prediction Model and Strategy Formulation Based on Grey System

In this paper, we study the optimal investment strategy between gold and Bitcoin, establish a market price forecasting system using the grey forecasting model GM (1,1), and calculate the optimal investment between gold and Bitcoin through the association rule algorithm. Optimal investment ratio, and then determine the maximum return. The establishment of predictive investment models helps investors to formulate optimal investment plans. The established model can use certain data from previous years to predict the future exchange rate prices of gold and bitcoin and help trading investors make optimal buying and selling decisions. It has a certain model planning ability for investment in real life and has specific practical significance for trade investors.

Charging infrastructure planning for ride-sourcing electric vehicles considering drivers’ value of time

ABSTRACT A reliable-charging network is urgently demanded to support electrified ride-sourcing services due to their shorter dwell time, longer daily vehicle miles traveled, and concerns of sacrificing revenue for charging activities. We developed an integer programming (IP) model for the optimal allocation of charging stations and charging plugs to minimize the total investment costs and spatio-temporal varying drivers’ value of time (VOT) for charging activities. The trip chain data of the RideAustin ride-sourcing services have been used as a test case, based on which we estimated the charging needs of ride-sourcing EVs and identified candidate charging locations to fulfill the daily travel needs of ride-sourcing drivers. Through numerical study and sensitivity analyses, we analyze the impacts of different charger types, fleet sizes, government incentives, and VOT considerations on the optimal investment plans and system costs, and show the importance of considering ride-sourcing drivers’ VOT into charging infrastructure planning.

EVALUATION OF GOVERNMENT INVESTMENT USING NESTED PROBABILISTIC LINGUISTIC PREFERENCE RELATIONS BASED ON GRAPH THEORY

Government investment, as a major government function, is closely related to national development and economic growth. It plays a key role to maximize the benefits of this fund, which requires the government to choose the optimal investment plan. Considering the complex and uncertain decision-making environment, we propose the nested probabilistic linguistic preference relation (NPLPR) based on the nested probabilistic linguistic term sets (NPLTSs), to express preference information from the qualitative and quantitative angle. According to graph theory, we define a consistency index and an acceptable consistency of NPLPR to measure the additive consistency. Based on which, we establish a novel algorithm for unacceptable consistent NPLPR to meet the acceptable consistency. Finally, projects in government investment are evaluated by the proposed decision-making method, and some comparative analyses, discussions, and implications are provided from three angles. This study provides a new perspective for scholars to make scientific and rational decisions with the help of technological and economic development in various fields.

A Stochastic-Robust Approach for Resilient Microgrid Investment Planning Under Static and Transient Islanding Security Constraints

When planning the investment in Microgrids (MGs), usually static security constraints are included to ensure their resilience and ability to operate in islanded mode. However, unscheduled islanding events may trigger cascading disconnections of Distributed Energy Resources (DERs) inside the MG due to the transient response, leading to a partial or full loss of load. In this paper, a min-max-min, hybrid, stochastic-robust investment planning model is proposed to obtain a resilient MG considering both High-Impact-Low-Frequency (HILF) and Low-Impact-High-Frequency (LIHF) uncertainties. The HILF uncertainty pertains to the unscheduled islanding of the MG after a disastrous event, and the LIHF uncertainty relates to correlated loads and DER generation, characterized by a set of scenarios. The MG resilience under both types of uncertainty is ensured by incorporating static and transient islanding constraints into the proposed investment model. The inclusion of transient response constraints leads to a min-max-min problem with a non-linear dynamic frequency response model that cannot be solved directly by available optimization tools. Thus, in this paper, a three-stage solution approach is proposed to find the optimal investment plan. The performance of the proposed algorithm is tested on the CIGRE 18-node distribution network.

신뢰도지표를 활용한 신 가치기반신뢰도평가 (VBDRA) 기반의 배전 설비 투자 선택 방안 연구

We select the optimal distribution system investment plan, which has the largest ratio of the financial benefit and investment cost. We measure the financial benefit of investment plan based on the improvement level of reliability index on the target distribution system. However, in the classical value based distribution system reliability assessment (VBDRA), the financial benefit was measured based on the reliability index itself, so our approach is a new VBDRA. The improvement level of reliability index is converted to the financial benefit by multiplying the electricity interruption cost. In this study, we also introduce the reliability index calculation method based on three basic reliability parameters: average failure rate, average power outage time, and average annual power outage time. Then, we calculate the reliability index of six different distribution systems. Finally, we measure reliability index and financial benefits of three virtual distribution systems with different investment plans on distribution components. Based on the ratio of financial benefits to investment costs, we select the optimal distribution system investment plan.

Affine decision rule approximation to address demand response uncertainty in smart Grids’ capacity planning

Generation expansion planning is a classical problem that determines an optimal investment plan for the expansion of electricity network. With the advent of demand response as a reserved capacity in smart power systems, recent versions of this class of problems model demand response as an alternative for the expansion of the network. This adds uncertainties, since the availability of this resource is not known at the planning phase. In this paper, we model demand response uncertainty in a multi-commodity energy model, called ETEM, to address the generation expansion planning problem. The resulting model takes the form of an intractable multi-period adjustable robust problem which can be conservatively approximated using affine decision rules. To tackle instances of realistic size, we propose a Benders decomposition that exploits valid inequalities and favors Pareto robustly optimal solutions at each iteration. The performance of our new robust ETEM is evaluated in a realistic case study that surveys the energy system of the Swiss “Arc Lémanique” region. Results show that an adjustable robust strategy can potentially reduce the expected cost of the system by as much as 33% compared to a deterministic approach when accounting for electricity shortage penalties. Moreover, an adjustable procurement strategy can be responsible for a 9 billion Swiss francs cost reduction compared to a naive static robust strategy. The proposed decomposition scheme improves the run time of the solution algorithm by 40% compared to the traditional Benders decomposition. To conclude, we provide a discussion on other possible problem formulations and implementations.

Optimal analysis for facility configuration and energy management on electric light commercial vehicle charging

Whilst the widespread adoption of electric vans is necessary to improve urban air quality and reduce carbon emissions, it is also self-evident that adequate charging stations are a precondition. However, the investment case for basic charging stations without public subsidies is challenging. In the context of a London case study, four business models are compared, which integrate solar power generation and new/second-life battery storage system with the basic charging facilities. Considering the uncertainties of electricity tariff and solar generation, the optimal infrastructure investment and operational planning has been formulated as a two-stage stochastic optimization model. The results show that: (i) in the integrated business models, the return on investment and charger installations could be increased by up to 5.39% and 17.06% respectively, and the carbon intensity could be reduced by up to 8.13%; (ii) the nondiscriminatory grant annualized as 50 £ is not sufficient, and a differentiated government subsidy policy may be more conducive to achieving a positive return on investment, such as 50 £ for fast chargers and 100 £ for rapid chargers; (iii) in the integrated business models, fast chargers undertake more vehicle-to-grid electricity exchange with the pattern adoption rate increased by up to 52.38%, while rapid chargers mainly ensure the timely charging completion with the usage frequency increased by up to 2.82%.

National development level effects on capital budgeting practices: a comparative study of nature vs nurture

Purpose This research should help determine whether development should focus on individual firms or will raising the national development level act like a rising tide and raise the performance of all corporations. Design/methodology/approach The comparative data used in this study come from 150 Australian (ASX200 index listed) firms and 150 Sri Lankan (Colombo Stock Exchange listed) firms. The research questions are answered via a quantitative research design that uses primary and secondary data. Findings The findings demonstrate that capital budgeting practices are more influenced by contingency features and sophistication in Australia and Sri Lanka. Also, Australian firms tend to use capital budget models with good-to-strong predictive power (except for ROE) and Sri Lankan firms tend to use capital-budget models with fair-to-poor predictive power. Further, the analysis of Australian firms yielded much stronger and more statistically significant results than the analysis of Sri Lankan firms. Practical implications In complex real-world situations, reconciling the outputs of a multifaceted approach to capital budgeting methods is more likely to give the depth and width of input needed to achieve an optimal capital investment plan. Originality/value The results of this study can provide rich information for stakeholders about new findings in capital budgeting (CB) practices and their contributions to firm performance in a comparative perspective.

СТАТИСТИЧНИЙ АНАЛІЗ ІНВЕСТИЦІЙНОЇ ДІЯЛЬНОСТІ ПІДПРИЄМСТВ МОРСЬКОГО ТРАНСПОРТУ З ВИКОРИСТАННЯМ МЕТОДІВ ФІНАНСОВОЇ МАТЕМАТИКИ

This article examines the relevance of statistical analysis of the investment activity of maritime transport enterprises based on the methods of financial mathematics. Financial mathematics, as you know, is the quantitative part of financial analysis, which is aimed at solving a wide range of issues. Calculation of future money can be attributed to the elementary problems of financial mathematics, and the decisions related to investment are among the complex problems. The purpose of this article is to conduct a static analysis of the investment activity of maritime transport enterprises and develop a model for optimizing the investment strategy in maritime transport. Despite the large output in this area, the areas of statistical analysis and optimization of investment activities have not been sufficiently studied, especially in maritime transport enterprises. Therefore, it is important to systematize the approach to information collection and statistical data analysis with the aim of creating a clear information base, on the basis of which it is possible to increase the volume of investments. During the research, the following statistical methods were used, namely, methods of analysis and synthesis - when conducting a statistical assessment of capital investments; comparison and generalization – to establish the relationship between value, information and risk; factor modeling - for the development of optimization of inflationary processes based on the methods of financial mathematics. In this article, a statistical analysis of the optimization model of the investment strategy in maritime transport was carried out. As a result, the obtained optimal investment plan is proposed to be used in maritime transport enterprises, namely in ports and stevedoring companies, it leads them to a kind of highway that should be followed in this environment. Thus, the article revealed the existing problems of investment activities at maritime transport enterprises, proved that for an effective investment strategy, it is necessary to take into account the assessment of risks associated with the investment of funds and the statistical analysis of investment projects with the involvement of financial mathematics methods.