Economic Optimization Research Articles

Study of Two-Stage Economic Optimization Operation of Virtual Power Plants Considering Uncertainty

Study of Two-Stage Economic Optimization Operation of Virtual Power Plants Considering Uncertainty

Repurposing coal plants into thermal energy storage—a techno-economic assessment in the Indian context

Abstract Substantial coal phase out initiatives have been growing as the world mobilizes to meet the Paris climate goals. However, the stranded asset risk associated with this critical transition could fall disproportionately on Asian economies with younger coal fleets, like India. Here, we undertake plant-level techno-economic analysis to explore the value of installing commercially available, molten-salt thermal energy storage (TES) systems for repurposing existing coal power plants in the Indian context. We combine process simulation and an economic optimization model to evaluate design and operations of TES systems for a variety of technology assumptions, coal plant archetypes, and electricity price scenarios. Key drivers of economic viability identified include longer remaining plant lifetime, increasing peak TES temperature, lower TES energy capacity cost, co-production of waste heat for end-uses, and increasing temporal variability of electricity prices. The plant-level analysis was then extended to screen for the potential of TES retrofits within the coal power fleet in Uttar Pradesh, the most populous Indian state with a significant share of India’s coal capacity. Analysis for a single electricity price scenario indicates that over 82% of the coal units in the state can be retrofitted and recover the installed costs of TES retrofits, provided that fixed operating and maintenance costs are excluded. These results reinforce the opportunity for decision-makers to consider TES retrofits of coal plants into cost-effective grid decarbonization strategies.

Effect of density and thickness on sound absorption performance of flexible porous material-aerogel

Abstract Aerogel is recognized for its effective sound absorption capabilities, which contribute to its widespread application in reducing vibrations and noise. Consequently, it is often employed as a sound-absorbing material in various settings, including industrial machinery and the construction sector. In this research, we conducted an experimental analysis and employed standing wave tube testing to evaluate the sound absorption coefficient of aerogel. The investigation specifically focused on the impact of density and thickness on the sound absorption performance of aerogel. Through this process, we established the optimal structural parameters for aerogel to maximize its sound absorption efficiency. The study’s findings indicate that the sound absorption coefficient of aerogel sees a swift rise as its density increases from 0.4g/cm3 to 0.7g/cm3, with a subsequent deceleration in the rate of increase beyond the 0.7g/cm3 threshold. Concerning thickness, the sound absorption coefficient also experiences a rapid increase as the aerogel’s thickness is below 20mm, after which the rate of increase diminishes for thicknesses above 20mm. By considering both sound absorption performance and economic optimization, the research identifies a density of 0.7g/cm3 and a thickness of 20mm as the optimal parameters for achieving the best sound absorption with aerogel.

Economic Operation Optimization Under Real-Time Pricing for an Energy Management System in a Redundancy-Based Microgrid

Economic Operation Optimization Under Real-Time Pricing for an Energy Management System in a Redundancy-Based Microgrid

Optimized scheduling of integrated energy systems for low carbon economy considering carbon transaction costs

With the introduction of the “dual carbon” goal and the continuous promotion of low-carbon development, the integrated energy system (IES) has gradually become an effective way to save energy and reduce emissions. This study proposes a low-carbon economic optimization scheduling model for an IES that considers carbon trading costs. With the goal of minimizing the total operating cost of the IES and considering the transferable and curtailable characteristics of the electric and thermal flexible loads, an optimal scheduling model of the IES that considers the cost of carbon trading and flexible loads on the user side was established. The role of flexible loads in improving the economy of an energy system was investigated using examples, and the rationality and effectiveness of the study were verified through a comparative analysis of different scenarios. The results showed that the total cost of the system in different scenarios was reduced by 18.04%, 9.1%, 3.35%, and 7.03%, respectively, whereas the total carbon emissions of the system were reduced by 65.28%, 20.63%, 3.85%, and 18.03%, respectively, when the carbon trading cost and demand-side flexible electric and thermal load responses were considered simultaneously. Flexible electrical and thermal loads did not have the same impact on the system performance. In the analyzed case, the total cost and carbon emissions of the system when only the flexible electrical load response was considered were lower than those when only the flexible thermal load response was taken into account. Photovoltaics have an excess of carbon trading credits and can profit from selling them, whereas other devices have an excess of carbon trading and need to buy carbon credits.

Economic Optimization and Performance Enhancement of Rooftop Solar Power Systems Using Concentrated Solar Technology and Advanced Materials

Abstract: This research investigates the economic optimization and performance enhancement of rooftop solar power systems through the integration of concentrated solar technology and advanced materials. The aim is to assess the viability and effectiveness of these innovations in both residential and commercial settings. By focusing on improving energy efficiency and reducing costs, the study provides a comprehensive economic perspective on the adoption of these advanced solar technologies. Key aspects of the research include the evaluation of lifecycle costs, energy yield, and payback periods, offering insights into the long-term financial benefits and feasibility of implementing such systems. The integration of concentrated solar technology enhances the intensity of solar energy captured, thereby significantly boosting the efficiency of rooftop solar panels. Advanced materials, such as perovskites, multi-junction cells, and nanomaterials, are examined for their potential to improve energy absorption, durability, and overall performance of solar panels. The research employs simulation software to model the performance of these optimized systems under various environmental conditions, providing a detailed analysis of their energy output and efficiency.

Impact of Financial Risk and Macro-Economic Variable on Stock Return: Evidence from Commercial Banks of Nepal

This paper examines the impact of financial risks and macroeconomic variables on the stock returns of commercial banks in Nepal. The study focuses on four main types of financial risks: credit risk, market risk, capital risk, and liquidity risk, alongside bank size, return on assets (ROA), and dummy variables for COVID-19 and earthquake impacts. Additional macroeconomic variables include economic growth, inflation, and interest rates. Using panel data regression on yearly data over 11 years for 20 commercial banks, the study investigates these factors' causal impacts on stock returns. The findings indicated that capital risk and liquidity risk negatively affect stock returns, suggesting that higher capital adequacy ratios limit high-risk, high-return investments, and higher liquidity risks lead to financial instability. Credit risk and market risk, however, did not impact stock returns. Larger banks faced negative stock returns, likely due to management complexities and inefficiencies. Economic growth and inflation positively impacted stock returns, reflecting economic optimism and increased activities, while higher interest rates negatively affected returns by increasing borrowing costs. The COVID-19 pandemic had a positive impact due to increased stock market activity amid limited investment opportunities, whereas the 2015 earthquake negatively affected returns as efforts focused on rebuilding. These insights underscore the need for robust risk management and consideration of broader economic conditions in investment and policy decisions.

Automatic response framework for large complex natural gas pipeline operation optimization based on data-mechanism hybrid-driven

In recent years, the scale of gas transmission networks has been continuously expanding, and the operational conditions are becoming increasingly complex. This poses higher requirements for the centralized control of the pipeline network operation. Relying solely on the experience of scheduling personnel may not comprehensively address the operational issues within the network. There is an urgent need for efficient automatic response methods (ARM) to assist in formulating operation schemes and ensuring the safe and stable operation of the pipeline network. Therefore, this paper proposes an automatic response framework of large complex natural gas pipeline operation optimization based on data-mechanism coupling to provide operation schemes that ensure safety, stability, and economic benefits. First, two ARM is proposed, namely the Data-Based ARM using operation scheme database, and the Opti-Model ARM based on optimization modeling. Subsequently, the rapid response features of Data-Based ARM and the optimal response characteristics of Opti-Model ARM are combined to establish the Integrated ARM. Finally, these three ARMs are compared and analyzed through a regional natural gas pipeline network in China. The result indicates that the Data-Based ARM can quickly produce a variety of matched solutions but cannot ensure economic optimality. Response solutions obtained through Opti-Model ARM reduce the compressor energy costs by 4.6–10.8 % compared to on-site operational schemes, but they take longer in response time. In contrast, the economic attributes of solutions derived from the Integrated ARM are on par with Opti-Model ARM, but with a 58.7 % improvement in response speed. The proposed Integrated ARM can swiftly and accurately offer economically viable operation schemes tailored to the varying needs of pipeline operators, which can help to address the energy demand challenges of the future, fostering sustainable development.

Influence of calcination temperature and particle size distribution on the physical properties of SrFe12O19 and BaFe12O19 hexaferrite powders

The paper deals with the economic optimisation of ferrite powder preparation during producing hard ferrite magnets. The magnetic properties of ferrites are investigated by replacing feedstock and reducing calcination temperature and particles in the order of tens of microns. The granulates about 8–10 mm in size were calcined for 2 h in the temperature range from 1100 °C to 1300 °C and additionally crushed and milled to an average particle size of about 80–90 µm. The scanning electron microscopy images confirmed the agglomerates of particles with different shapes and sizes in tens of µm. The X-ray diffraction measurements revealed that, besides the SrFe12O19 and BaFe12O19 phases, there was also the presence of 2–39% hematite. The highest values of maximum energy product (BH)max = 930 J/m3 and remanent magnetic induction Br = 72.8 mT were obtained at a calcination temperature of 1300 °C. The Henkel plots confirmed the presence of exchange-coupling and dipolar magnetic interactions at lower and higher magnetic fields, respectively. The strength of interactions was also dependent on the calcination temperature. Replacing strontium with barium led to a deterioration of the magnetic parameters, which were optimal at a lower calcination temperature (1100 °C). This phenomenon was partly overcome by reducing the mean particle size of Ba-based hexaferrites to 45–50 µm.

A distributed economic model predictive control-based FPPT scheme for large-scale solar farm

Increasing solar-photovoltaic-power penetration necessitates the implementation of flexible power point tracking (FPPT) in solar farms. However, coordinating multiple solar photovoltaic (PV) power generation systems (SPVPGSs) poses a significant challenge due to their inherent intermittency and varying dynamic characteristics, complicating FPPT implementation. To overcome this challenge, this paper proposes a distributed economic model predictive control (DEMPC) scheme to achieve FPPT, while simultaneously enhancing overall economic performance in solar farms. By integrating solar farm control and local control into one optimal control framework, this scheme eliminates the need for power allocation, PV voltage reference calculation, and pulse width modulation. Leveraging the SPVPGS model and soft power constraint, DEMPC controllers are designed to achieve the economic targets of solar farms, which cooperate through a communication network to realize FPPT and economic optimization. Additionally, the strong nonlinearity of SPVPGS causes a non-convex mixed integer nonlinear programming (MINLP) problem, solved by a MINLP algorithm using finite converter switching states. Simulations under step-changed irradiance and power reference, as well as urgent maintenance conditions, demonstrate that the DEMPC-based FPPT scheme significantly outperforms the traditional hierarchical model predictive control-based FPPT scheme, presenting both superior dynamic response and enhanced economic performance in FPPT implementation.

Evaluation of energy economic optimization models using multi-criteria decision-making approach

Achieving high performance in energy systems is crucial for sustainability. Energy economy optimization (EEO) models offer transparent analysis for energy policy decision-making. However, evaluating and benchmarking these models is a complex multicriteria decision making (MCDM) problem. Challenges include multiple criteria, data variation, and the importance of diverse criteria. This study develops an integrated MCDM approach to evaluate and benchmark EEO models. The methodology involves three phases. First, 12 commonly used EEO models and five evaluation criteria (software licenses, public source code, redistribution, public source data, and commercial software) are identified to create an evaluation decision matrix. Second, the fuzzy-weighted zero-consistency method (FWZIC) is used to evaluate and assign weights to the criteria. These weights are utilized in the benchmarking phase. Third, individual and group fuzzy decision by opinion score method (FDOSM) techniques are integrated to benchmark the EEO models based on the weights acquired. The FWZIC weighting reveals that the public source code criterion has the highest weight (0.3347), while redistribution has the lowest weight (0.1021). The group FDOSM results show that the OSeMOSYS model ranks first with the highest score (0.1595), while the DNE21+, MARIA, and MESSAGE models have the lowest score (0.0646), ranking them last. Systematic ranking, sensitivity ranking, and comparative analysis verify the proposed evaluation and benchmarking framework.

Energy, exergy, economic, and environmental analyses and optimization of a multigeneration system considering high-temperature island environment

Renewable energy based multigeneration systems help satisfy the diverse demand for marine resources exploration in off-grid island area, where the storage of fluctuating renewable energy for continuous power supply becomes necessary. Therefore, this paper proposes a solar-driven multigeneration system for island environment integrated with energy storage and production of electricity, cooling, heating, and freshwater. The system comprises an ejector power subsystem, a multiple-effect distillation subsystem, and a CO2 binary mixture-based compressed gas energy storage subsystem. The system performance from the perspectives of energy, exergy, economics, and environment, as well as the impact on performance of the working fluid and several key system parameters, are investigated in this paper. According to the results, an increase in the mass fraction of organic in the CO2 mixture leads to an improvement in system energy efficiency and exergy density at the cost of lower system power efficiency. Increasing the high-pressure tank pressure enhances system performance except for system power efficiency. Increased low-pressure tank pressure leads to improved system energy efficiency and exergy density, accompanied by lower system power efficiency and exergy efficiency. When the ambient temperature increases from 298 K to 303 K, the system energy efficiency noticeably decreases, whereas an increase in direct normal irradiance improves various system performance indicators. Among several mixtures, CO2/R32 mixture was chosen for further analysis and optimization for its wider operating range with R32 mass fraction and better system performance. The optimal operating point had system energy efficiency, exergy density, and modified levelized cost of energy of 1.281, 22.216 kWh/m3, and 0.131 $/kWh, respectively. The proposed system in this research is proven to be a novel technical solution to meet the diverse energy demands on unique island environments.

Study on the structure parameters and design method optimization of blade-type screw steel pipe pile

The rationality of structure parameters of the blade-type screw steel pipe pile is the major factor in determining the safety, applicability, and economy of a pile foundation, but the existing design methods have not defined the specific approaches to calculating structure parameters of blades and steel pipes. To address this issue, an analysis of the strength theory was performed on spatial helical blades, revealing the ratio of blade width to steel pipe radius as the core index to do the structure parameters design of blade-type screw steel pipe pile and established a mathematical calculation model between the vertical bearing capacity of single pile and a. The rational value of a is 2.0–3.0 using numerical calculation. The correlation between blade thickness and wall thickness of steel pipes was determined using the force analysis based on the synergy between blades and steel pipes. Then the structure parameters design method of blade-type screw steel pipe pile with coordinated parameters was proposed. The rationality and superiority of this design method were verified by comparing the existing test data and the finite element simulative analysis. Based on this design method and the current specifications, such as the Technical Code for Building Pile Foundations, a safe, applicable, and economical optimization design method and flow of blade-type screw steel pipe piles were further proposed for engineering design reference.

Simulation of Optimization Decision Model for Low-carbon Economy Schemes Based on Machine Learning Algorithms

Low-carbon economy (LCE) is a shift from high-carbon intensive industries and activities to low-emission or zero-emission industries and activities, which requires a lot of investment in new technologies and infrastructure, as well as changes in policies and regulations to promote sustainability and reduce emissions. In this article, the back propagation neural network (BPNN) algorithm in machine learning (ML) is applied to the construction of low-carbon economic scheme optimization decision model, and the performance of the model is simulated and tested. The results show that BPNN algorithm has higher accuracy and reliability in predicting the development level of LCE, which is 23.8% higher than Support Vector Machine (SVM) algorithm. Through the comparative study of BPNN algorithm and SVM algorithm in forecasting the development level of LCE, the advantages of BPNN algorithm in forecasting accuracy and application value are verified. BPNN algorithm can better adapt to and deal with the complex data patterns in the field of LCE, improve the accuracy and reliability of prediction, and provide strong technical support and guarantee for the growth of LCE.

Economic Optimization of Thermal Insulation Thickness for Insulated and Electrically Traced Pipelines in Drilling Applications

This study presents an economic optimization model for determining the optimal insulation thickness for both thermal insulation and electric tracing pipelines. Using Life-Cycle Cost (LCC) analysis, optimization research was conducted under various working conditions to identify the most cost-effective insulation thickness. Factors such as pipe diameter, operational duration, drilling fluid temperature, and heat cost were analyzed to assess their impact on the economic thickness of the insulation layer, specifically within the unique environment of drilling sites. The results provide the economic thickness and total cost for both insulated and electrically traced pipelines under different scenarios. For instance, for a DN100 pipe with rock wool insulation operating for 3600 h, the economic thickness of the electrically traced pipe insulation was determined to be 5.18 cm greater per unit length compared to the non-electrically traced pipe, resulting in an additional cost of 19.36 CNY/m. These findings offer valuable insights for optimizing pipeline insulation in drilling applications.

Economic Optimization of Internal Thermally Coupled Air Separation Column Configurations

AbstractAn internal thermally coupled air separation column (ITCASC) process is an effective energy‐saving technology in the air separation process. However, a large economic investment is a crucial factor for the widespread use of this technology in practical applications. In this article, an alternative configuration design, namely, top‐integrated (T‐ITCASC), bottom‐integrated (B‐ITCASC), and top‐bottom‐integrated ITCASC (T‐B‐ITCASC) with a focus on energy savings and economic feasibility are studied. A rigorous optimization based on a nonlinear interior‐point algorithm was developed by integrating the dynamic model into the optimization formulation. In the context of ITCASC process design and optimization, numerical simulations demonstrated that T‐ITCASC, B‐ITCASC, and T‐B‐ITCASC configurations improved energy‐saving potential and reduced capital investment, compared to the F‐ITCASC and conventional air separation column (CASC) configurations. Among these optimized configurations, the T‐B‐ITCASC configuration is preferred.

Optimizing environmental costs and economic benefits in new energy vehicle production: A case study of FAW Hongqi in China

This study focuses on the environmental cost accounting and economic benefit optimization of China’s FAW Hongqi New Energy Vehicle manufacturing enterprise under uncertain conditions, within the context of the emission permit system This study calculates the pollution situation throughout the manufacturing and production process of FAW Hongqi new energy vehicles, and constructs a multi-level environmental cost evaluation system for FAW Hongqi new energy vehicle manufacturing projects. Through the interval fuzzy model of FAW Hongqi new energy vehicle manufacturing projects, the maximum economic benefits of the enterprise are simulated. The research results indicate that the pollution emissions of enterprises are mainly concentrated in the three processes of welding, painting, and final assembly. Enterprises use their own exhaust gas and wastewater treatment devices to meet the standards for pollution emissions. At the same time, solid waste generated during the automobile manufacturing process is handed over to third-party companies for treatment. Secondly, based on the accounting results of enterprise pollution source intensity and a multi-layer environmental cost evaluation system, the environmental costs of enterprises are accounted for, and the environmental costs are represented in interval form to reduce uncertainty in the accounting process. According to the accounting results of enterprise environmental costs, the main environmental costs of enterprises are environmental remediation costs caused by normal pollution discharge and purchase costs of environmental protection facilities. Pollutant emission taxes and routine environmental monitoring costs are relatively low. Enterprises can adopt more scientific solutions from the aspects of environmental remediation and environmental protection facilities to reduce environmental costs. After optimization by the fuzzy interval uncertainty optimization model, the economic benefits of the FAW Hongqi new energy vehicle manufacturing project were [101,254.71, 6278.5413] million yuan. Compared with the interval uncertainty optimization model, the lower bound of economic benefits increased by 57.68%, and the upper bound decreased by 12.08%, shortening the results of the economic benefits interval. Clarify the current environmental pollution situation of FAW Hongqi’s new energy vehicle manufacturing enterprise, provide data support for sustainable development of the enterprise, and provide reasonable decision-making space for enterprise decision-makers.

Optimization of electric vehicle design problems using improved electric eel foraging optimization algorithm

Abstract This paper introduces a novel approach, the Modified Electric Eel Foraging Optimization (EELFO) algorithm, which integrates artificial neural networks (ANNs) with metaheuristic algorithms for solving multidisciplinary design problems efficiently. Inspired by the foraging behavior of electric eels, the algorithm incorporates four key phases: interactions, resting, hunting, and migrating. Mathematical formulations for each phase are provided, enabling the algorithm to explore and exploit solution spaces effectively. The algorithm’s performance is evaluated on various real-world optimization problems, including weight optimization of engineering components, economic optimization of pressure handling vessels, and cost optimization of welded beams. Comparative analyses demonstrate the superiority of the MEELFO algorithm in achieving optimal solutions with minimal deviations and computational effort compared to existing metaheuristic methods.

Deep Low-Carbon Economic Optimization Using CCUS and Two-Stage P2G with Multiple Hydrogen Utilizations for an Integrated Energy System with a High Penetration Level of Renewables

Integrating carbon capture and storage (CCS) technology into an integrated energy system (IES) can reduce its carbon emissions and enhance its low-carbon performance. However, the full CCS of flue gas displays a strong coupling between lean and rich liquor as carbon dioxide liquid absorbents. Its integration into IESs with a high penetration level of renewables results in insufficient flexibility and renewable curtailment. In addition, integrating split-flow CCS of flue gas facilitates a short capture time, giving priority to renewable energy. To address these limitations, this paper develops a carbon capture, utilization, and storage (CCUS) method, into which storage tanks for lean and rich liquor and a two-stage power-to-gas (P2G) system with multiple utilizations of hydrogen including a fuel cell and a hydrogen-blended CHP unit are introduced. The CCUS is integrated into an IES to build an electricity–heat–hydrogen–gas IES. Accordingly, a deep low-carbon economic optimization strategy for this IES, which considers stepwise carbon trading, coal consumption, renewable curtailment penalties, and gas purchasing costs, is proposed. The effects of CCUS, the two-stage P2G system, and stepwise carbon trading on the performance of this IES are analyzed through a case-comparative analysis. The results show that the proposed method allows for a significant reduction in both carbon emissions and total operational costs. It outperforms the IES without CCUS with an 8.8% cost reduction and a 70.11% reduction in carbon emissions. Compared to the IES integrating full CCS, the proposed method yields reductions of 6.5% in costs and 24.7% in emissions. Furthermore, the addition of a two-stage P2G system with multiple utilizations of hydrogen further amplifies these benefits, cutting costs by 13.97% and emissions by 12.32%. In addition, integrating CCUS into IESs enables the full consumption of renewables and expands hydrogen utilization, and the renewable consumption proportion in IESs can reach 69.23%.

Decision-focused learning under decision dependent uncertainty for power systems with price-responsive demand

Contemporary power systems are experiencing a growing integration of flexible resources on the consumer side. Different from flexible demand that submits specific bids to energy markets, price-responsive demand (PRD) adjusts its power consumption without notice, simply based on the resulting electricity prices as well as internal priorities and limitations. In this paper, we demonstrate how a high penetration of PRD (the behavior of which is invisible to the operator during the day-ahead unit commitment stage) results in systematic inefficiency costs and formulate the so-termed decision-focused learning problem of learning to provide a demand forecast which, once fed as an input to the operator’s economic dispatch optimization problem, results in an efficient dispatch. Interestingly, the prescribed demand forecast affects the resulting prices, which in turn affect the actual demand realization, giving rise to a decision-dependent uncertainty. Motivated by the problem’s hard-to-evaluate objective function, we solve it using Bayesian optimization. The empirical evaluations demonstrate significant savings in the effective real-time system cost, compared to the current practice of using the default demand forecast. Moreover, the method is shown to achieve a system cost that is fairly close to the one achieved by a system that fully integrates PRD into the day-ahead process; but without requiring any change in the operator’s existing dispatch algorithm while avoiding all efforts necessary for the integration of flexible demand, which is a widely pursued field of ongoing research.