Carbon Emission Cost Research Articles

Optimal Scheduling of Source–Load Synergy in Rural Integrated Energy Systems Considering Complementary Biogas–Wind–Solar Utilization

To address the issues of the low usage efficiency and illogical structure in rural regions, this study builds a rural integrated energy system (RIES) that incorporates the complementary use of biogas, wind, and light. For resolving the RIES optimum-low-carbon-economic-dispatch problem, a source–load-cooperative optimal-dispatch strategy is proposed. Firstly, a multi-energy integrated demand response (IDR) model based on time-of-use tariffs and time-varying biogas costs is established on the demand side. Secondly, power-to-gas devices are added on the supply side to optimize the system’s electricity–gas-coupling relationship and increase the wind power output space. Thirdly, an RIES-oriented carbon-trading model is constructed by considering the actual carbon emissions of gas loads and the stepped-carbon-trading mechanism. Finally, an optimal-dispatch model is built with the objective function of reducing the total energy cost, wind abandonment cost, IDR cost, and carbon emission cost, while the problem is transformed into a mixed-integer linear problem and solved using CPLEX 12.9. By setting up four scenarios for example analysis, the results show that on typical days in spring, summer, autumn, and winter, the total operating costs of the stepped-carbon-trading system (Scenario 1), taking into account the source-side power-to-gas (P2G) device and the load-side IDR, are reduced by 12.25%, 11.25%, 12.42%, and 11.56%, respectively, compared to the system without the introduction of the IDR (Scenario 3). In contrast to the system that lacks a P2G device at the source end (Scenario 2), the overall costs are decreased by 4.97%, 3.07%, 5.02%, and 5.36%, but the wind power consumption rates are increased by 11.63%, 7.93%, 11.54%, and 11.65%, respectively. Stepped emission trading (Scenario 1) reduces the total operating costs by 5.12%, 3.15%, 5.21%, and 6.84%, respectively, while reducing the biogas costs by 9.75%, 7.74%, 9.67%, and 9.57%, respectively, in comparison to traditional emission trading (Scenario 4). The example results demonstrate the economics, effectiveness, and reliability of a stepped-carbon-trading system with an integrated P2G load-side energy demand response.

Bi-Level Planning of Electric Vehicle Charging Stations Considering Spatial–Temporal Distribution Characteristics of Charging Loads in Uncertain Environments

With the increase in the number of distributed energy resources (DERs) and electric vehicles (EVs), it is particularly important to solve the problem of EV charging station siting and capacity determination under the distribution network considering a large proportion of DERs. This paper proposes a bi-level planning model for EV charging stations that takes into account the characteristics of the spatial–temporal distribution of charging loads under an uncertain environment. First, the Origin–Destination (OD) matrix analysis method and the real-time Dijkstra dynamic path search algorithm are introduced and combined with the Larin Hypercube Sampling (LHS) method to establish the EV charging load prediction model considering the spatial and temporal distribution characteristics. Second, the upper objective function with the objective of minimizing the cost of EV charging station planning and user charging behavior is constructed, while the lower objective function with the objective of minimizing the cost of distribution network operation and carbon emission cost considering the uncertainty of wind power and photovoltaics is constructed. The constraints of the lower-layer objective function are transformed into the upper-layer objective function through Karush–Kuhn–Tucker (KKT) conditions, the optimal location and capacity of charging stations are finally determined, and the model of EV charging station siting and capacity determination is established. Finally, the validity of the model was verified by planning the coupled IEEE 33-node distribution network with the traffic road map of a city in southeastern South Dakota, USA.

Location-Routing Optimization for Two-Echelon Cold Chain Logistics of Front Warehouses Based on a Hybrid Ant Colony Algorithm

Diverse demands have promoted the rapid development of the cold chain logistics industry. In the paper, a novel approach for calculating the comprehensive carbon emission cost was proposed and the front warehouse mode was analyzed under the background of energy conservation and emission reduction. To solve the two-echelon low-carbon location-routing problem (2E-LCLRP), a mathematical model considering operating cost, total transportation cost, fixed cost, refrigeration cost, cargo damage cost, and comprehensive carbon emission cost was proposed to determine the minimum total cost. A hybrid ant colony optimization (HACO) algorithm based on an elbow rule and an improved ant colony optimization (IACO) algorithm was proposed to solve the 2E-LCLRP. According to the elbow rule, the optimal number of front warehouses was determined and an IACO algorithm was then designed to optimize vehicle routes. An adaptive hybrid selection strategy and an optimized pheromone update mechanism were integrated into the HACO algorithm to accelerate convergence and obtain global optimal solutions. The proposed model and algorithm were verified through the case study of the 2E-LCLRP in Nanjing, China. The HACO algorithm outperformed the original ant colony optimization (ACO) algorithm in terms of convergence rate and solution quality. This study provides significant insights for enhancing heuristic algorithms as well as valuable research methods. Furthermore, the results can help cold chain logistics companies in balancing economic costs and environmental benefits and address cold chain distribution of agricultural products.

Low‐carbon demand response program for power systems considering uncertainty based on the data‐driven distributionally robust chance constrained optimization

AbstractThe demand response (DR) program is an effective solution to promote the low‐carbon operation of power systems with increasing penetration of renewable energy sources (RESs). This paper proposes a low‐carbon DR program for power systems to enhance both the environmental friendliness and uncertainty resilience of the system operation. The system operator aims to minimize both the system's operation cost and carbon trading cost. To handle the uncertainty associated with stochastic RES generation power and load consumption power, a data‐driven method named the two‐sided distributionally robust chance‐constrained (TS‐DRCC) approach is adopted to enhance the system's uncertainty resilience. A ladder‐type carbon trading scheme is utilized to calculate the carbon emission cost of the system. Comprehensive analyses of case studies have been conducted to validate that the proposed strategy can effectively reduce the total carbon emissions and total operation costs with good uncertainty resilience performance. The proposed low‐carbon DR program is verified to achieve 63.64% more carbon emission reduction compared with the conventional DR program. Besides this, the proposed low‐carbon DR program can also achieve 4.39% carbon‐intensive generation power reduction and 5.52% RES power consumption compared with the conventional DR program.

Low-Carbon Economic Dispatch Model of Integrated Energy System Accounting for Concentrating Solar Power and Hydrogen-Doped Combustion

Against the background of carbon peak and carbon neutralization, in order to solve the problem of poor flexibility of integrated energy systems and wind power consumption while improving the potential of hydrogen energy emission reduction, this study proposes an integrated energy system that takes into account the coupling of concentrating solar power (CSP), hydrogen-doped combustion, and power-to-gas (P2G) conversion. Firstly, a mathematical model of a CSP-CHP unit is established by introducing a CSP power station, aiming at the defect of the “heat to power” mode in the CHP system. Secondly, the energy consumption of P2G hydrogen energy production is satisfied by surplus wind power. The utilization stage of hydrogen energy is divided into supply CHP combustion and CO2 methanation, forming a CSP-P2G-HCHP collaborative framework and establishing an IES low-carbon economic dispatch model with CSP-P2G-HCHP. At the same time, the carbon trading mechanism is introduced to constrain the carbon emissions of the system. Finally, an optimization strategy with the minimum sum of the operation and maintenance cost, the energy purchase cost, the wind curtailment cost, and the carbon emission cost as the objective function is proposed, and the CPLEX solver is used to solve and carry out multi-case analysis. The simulation results show that the carbon emissions are reduced by 6.34%, the wind curtailment cost is reduced by 52.2%, and the total cost is reduced by 1.67%. The model takes into account the carbon reduction effect and operating efficiency and effectively improves the new energy consumption capacity.

Flexible allocation and optimal configuration of multi-level energy exploitation units for heterogeneous energy systems considering resource distribution

The energy systems are evolving into heterogeneous energy systems (HESs) with complicated integration of sources, networks, loads, and storage towards a decarbonized future of human beings. This paper develops the flexible allocation and optimal configuration of multi-level energy exploitation units (MEEUs) for the HES considering resource distribution including local energy conditions and actual coupled relationship of various energy networks. A bi-level optimal planning model of MEEUs for the HES considering initial investment cost, carbon emission cost, operation cost and line loss is developed to figure out the optimal location, structure, configuration and corresponding operation strategy. To address the non-convex and non-linear model, a novel hybrid solution method considering the mixing characteristics of mixed-integer second-order cone programming (MISOCP) model and quadratic non-convex model is proposed. The solution method adopts MISOCP model for figuring out the optimal location, structure and configuration of MEEUs and quadratic non-convex model for optimal operation strategy of MEEUs, which not only improves the computational efficiency but also ensures the solution accuracy. Finally, the related case studies are conducted to compare the proposed MEEU and distributed energy storage unit (DESU) in terms of energy consumption, renewable energy absorbency, carbon emission and line loss. The results verify the significant improvement of the comprehensive energy efficiency, the investment potentiality and the environmental benefits obtained by the proposed planning model.

Research on multi-time scale integrated energy scheduling optimization considering carbon constraints

To address the challenge of grasping carbon emission costs in different dispatching cycles under multiple time scales of the Integrated Energy System (IES), a carbon-constrained multi-time scale optimization method for integrated energy systems is proposed. First, a novel mechanism was proposed, Carbon Constraint Mechanism, which includes carbon tax, green certificate trading, and tiered carbon trading mechanism. Second, the carbon constraint mechanism was introduced into multi time scale scheduling optimization, and an IES multi time scale low-carbon optimization scheduling model was established. Third, the economic cost, carbon emissions, and primary energy consumption were considered as objective functions in multi-objective optimal scheduling. Moreover, utilized day ahead scheduling results and real-time load forecasting to dynamically adjust the output power of IES equipment in combination with power change penalties and carbon constraints. The simulation results showed that, compared to dispatch optimization strategies that solely prioritized economy, this study reduced carbon emissions by 28.38 % across multiple time scales while enhancing the economic performance of IES by considering carbon constraints. The multi-time scale dispatch optimization, incorporating carbon constraint mechanisms, improved the system's stability and low-carbon economy, proving the feasibility and effectiveness of the proposed optimal dispatch strategy.

Green supply chain for steel raw materials under price and demand uncertainty

The scientific formulation of procurement and inventory plans is an important topic in the steel industry, when considering both economic and environmental impacts in an uncertain market environment. Robust optimization methods offer an effective solution to this problem. This study has investigated a multi-echelon and multi-period front-end supply chain centered on a steel enterprise under uncertain purchase prices and demand. The goal is to minimize the costs associated with mining, processing, transportation, procurement, and inventory along with the corresponding carbon emission costs. To address this issue, we present a robust rolling horizon model (RRHM) that considers the inventory capacity constraints of steel enterprise and incorporates ore grade requirements. Subsequently, the RRHM was reformulated into a solvable mixed-integer linear programming model, and the relationship between the robust model and a specific deterministic model was established. Finally, the model was validated by generating instances based on real scenarios. The results demonstrate that the robust rolling horizon model proposed in this paper outperforms both the Sample Average Approximation (SAA) strategy and the (s, S) strategy in terms of cost and robustness. In addition, a sensitivity analysis of the key parameters was conducted. The findings reveal that the unit inventory, mining, and carbon emission costs have a limited impact on the total cost, highlighting the robustness of the model. These findings provide valuable insights into the green and sustainable development of the steel industry, enabling the formulation of effective strategies to mitigate the impacts of uncertainties.

A Sustainable Supply Chain Model with a Setup Cost Reduction Policy for Imperfect Items under Learning in a Cloudy Fuzzy Environment

The present paper deals with an integrated sustainable supply chain model with the effect of learning for an imperfect production system under a cloudy fuzzy environment where the demand rate is treated as a cloudy triangular fuzzy (imprecise) number, which means that the demand rate of the items is not constant, and shortages and a warranty policy are allowed. The vendor governs the manufacturing process to serve the demand of the buyer. When the vendor supplies the demanded lot after the production of items, it is also considered that the delivery lots have some defective items that follow an S-shape learning curve. After receiving the lot, the buyer inspects the whole lot, and the buyer classifies the whole lot into two categories: one is the defective-quality items and the other is the imperfect-quality items. The buyer returns the defective-quality items to the seller after a screening process, for which a warranty cost is included. During the transportation of the items, a lot of carbon units are emitted from the transportation, damaging the quality of the environment. The seller includes carbon emission costs to achieve sustainability as per considerations. A one-time discrete investment is also included for the minimizing of the setup cost of the seller for the next cycles. We developed models for the scenario of the separate decision and for the integrated decision of the players (seller/buyer) under the model’s consideration. Our aim is to jointly optimize the integrated total fuzzy cost under a cloudy fuzzy environment sustained by the seller and buyer. Numerical examples, sensitivity, analysis limitations, future scope and conclusions have been provided for the justification of the proposed model, and the impact of the input parameters on the decision variables and integrated total fuzzy cost for the supply chain are provided for the validity and robustness of this proposed model. The effect of learning in a cloudy fuzzy environment was positive for this proposed model.

Preliminary investigation and life cycle assessment of artificial reefs with recycled brick-concrete aggregates

An economical and environment-friendly artificial reef concrete using fully recycled brick-concrete aggregate as raw material was proposed. This study investigates the mechanical properties, drying shrinkage, chloride ion penetration, and biological adaptability of the reef. A comparative analysis is conducted on the environmental impacts (EI), carbon emission impact and cost efficiency of artificial reefs, considering natural aggregate concrete and recycled brick-concrete aggregate concrete at the material level. The results indicate that the use of recycled brick-concrete aggregates can achieve service performance equivalent to that of natural aggregates. Field investigations involving samples immersed in seawater for six months reveal the positive trapping effects of recycled materials on aquatic organisms. The Life Cycle Assessment (LCA) results reveal that achieving a 100 % replacement rate of recycled brick concrete aggregate (RBCA) creates a positive feedback loop for carbon emissions when appropriately incorporating industrial waste materials like fly ash and silica fume. In comparison to ordinary concrete, the utilization of recycled materials can significantly reduce construction costs, up to 63.7 %, thereby presenting notable economic advantages.

An integrated vendor-buyer model with sustainability and remanufacturing of returned product

Carbon emission reduction is very crucial nowadays. A proper balance is required for carbon emission reduction and burning of fossils fuels because during fossil fuel burning carbon is emitted and it is essential for industrialization. This study presents an inventory model with green technology where production of new items and remanufacturing of returned items are allowed. Remembering the present global situation, the ordering cost is assumed as variable cost which contain order cancellation and reorder cost. Online order policy is considered with quality of product and a return rate. Keeping in mind the social, economic and environmental aspects, the demand function is considered as sustainability sensitive demand. A nonlinear model is designed based on the above concepts. The model is solved both theoretically and analytically. The main motives of the work are to find the effects of ordering cost, carbon emission cost and green technology investment on the inventory model for suggesting the best policy to the inventory manager. Sensitivity analysis is performed to validate the model. The numerical results prove that ignoring ordering cost, carbon emission cost and green technology investment can lead to a false optimal solution and this can be a huge economical loss for the inventory manager. The results also prove that a sustainable model with carbon emission and green technology investment is more realistic and profitable in compare with the other existing models.

Accounting for Carbon Emissions Among Large U.S. Companies: Does Materiality Matter

This paper investigates carbon emissions materiality with regards to financial statements and investigates potential methods of accounting for carbon emission rights. We investigate whether some of the largest U.S. companies should be required to discuss and quantify greenhouse gas emissions, as defined by the GHG Protocol, in the required 10-K annual report. Our analysis uses a new metric, based on social carbon cost, that suggests social cost of carbon emissions could be material, and supports the need for transparent accounting of the financial impact of carbon emissions in the required 10-K annual report. We searched each company’s 10-K for information on climate change and emissions-related disclosures. We found that while most companies acknowledge climate change as a risk factor, their 10-K narratives do not discuss or explain the impact of emissions on financial performance. Our findings suggest that climate-related factors are not being appropriately captured in current 10-Ks despite increasing calls for greater transparency on climate-related exposure, raising concerns that investors are not receiving the appropriate information necessary to evaluate investments. Providing such information would allow investors to gain a better understanding of company risks, and enhance their ability to make better investment choices.

Configuration and Operation Optimization for Industrial Steam Power System Sustainable Retrofit Considering Renewable Energy Integration

AbstractRenewable energy integration and operational optimization are crucial in energy sustainability and decarbonization, especially for industrial steam power systems (SPS). This study establishes an SPS superstructure that integrates wind, solar, and biomass energy. A mixed‐integer nonlinear programming (MINLP) model is developed to determine an optimal retrofit strategy that minimizes the life cycle cost, which includes carbon emission cost and energy cost. To solve this high‐dimensional complex optimization problem, a multistage strategy fusion differential evolution algorithm with dynamic partitioning of the SVM feasible domain (SVM‐MS‐DE) is developed. The results from the optimal strategy demonstrate a 9.02% reduction in the system's total cost and a 9.64% decrease in carbon emission through the incorporation of wind and solar energy. Additionally, the sensitivity analysis on biomass ratio, carbon emission price, energy demand, and carbon emission limits reveal that the region can contribute significantly to renewable energy initiatives. Several recommended policies are provided to encourage enterprises to move towards sustainable development.

Low-Carbon Mixed Traffic Route Recommendation for Community Residents Based on Multilayer Complex Traffic Network

With the proposed “carbon peaking” and “carbon neutral” goals in China, the transportation sector, as the second largest consumer of oil and a major producer of greenhouse gases, is a critical area for energy efficiency and emission reduction actions. However, few studies have focused on the effective combination of solving residents' commuting challenges and low-carbon travel. In this paper, by extracting real traffic flow data from taxi and bike-sharing trajectory data, a multilayer complex traffic network is formed to realize an interactive visual exploration of urban traffic patterns. Based on this network a low-carbon travel route recommendation is implemented using a modified genetic algorithm to reduce personal carbon emission and travel costs. Meanwhile, the trip chain level carbon emission estimation method is defined for city streets and recommended routes. With the integration of the above algorithms, a visual analytics system is designed and implemented to support the joint exploration of urban traffic patterns and the street carbon emission distribution, low-carbon mixed traffic route recommendations for inter-community commuting, and optimization of low-carbon recommended routes by adjusting bike stations. Take the taxi and bike-sharing trajectory data in Xiamen, China as an example, an evaluation analysis of the system shows that the method is effective in reducing commuting costs for community residents while reducing personal travel carbon emission.

A Supply Chain Coordination Optimization Model with Revenue Sharing and Carbon Awareness

The present study explores the impact of carbon emissions on supply chain coordination, where the supply chain entities are a retailer and a distributor. The study also involves two types of systems, namely centralized and decentralized. A centralized system computes the profit of the entire supply chain, including the profit of a retailer and a distributor, using the traditional optimization technique. In contrast, a decentralized system computes the profit of both a retailer and a distributor independently and uses the Stackelberg sequence for profit optimization. According to the Stackelberg sequence, one entity is considered a leader and the other a follower. When the profit in both systems is compared, it is found to be higher in the centralized system. So, to coordinate the system, a revenue-sharing contract is applied to coordinate the supply chain under a stock–time–price-sensitive demand rate. Finally, a carbon emission cost is implemented to the profits of both systems to make the model more sustainable. The main objective of the research is to optimize the profit of the supply chain by considering the concept of revenue-sharing contracts and making the system more sustainable through the implementation of carbon emission cost. The overall study concludes that the revenue-sharing fraction ‘δ’ helps in coordinating the system and 0.4 is the value of the revenue-sharing fraction ‘δ’ that perfectly coordinates the system. Due to this coordination, both the parties will gain profit, i.e., retailer and distributor, and this whole phenomenon increases the profit of the supply chain. A sensitivity analysis is also performed to check the stability of the model, and the model is found to be quite stable. A numerical example is illustrated, providing the result of the model.

The Impact of Carbon Emissions on Corporate Cost of Debt (COD): A Research on Borsa İstanbul (BIST) Sustainability Index

This study examines the influence of carbon emissions of companies traded on the BIST Sustainability Index on the cost of debt for 2017-2021. In other words, the study aims to guide decision-makers towards carbon emissions reduction by showing a significant impact between the cost of debt and carbon emissions. The data were obtained from the Public Disclosure Platform, companies’ financial statements, annual reports, sustainability reports, integrated reports, and the DataStream database and were subjected to statistical analysis. Panel data pooled OLS method was used in the study. The study found a significant impact of total carbon emissions, carbon emission intensity, leverage, and structure variables on the cost of debt. However, it was concluded that the size, return on assets, growth, and cash flow variables did not significantly impact the cost of debt.

Research on end-route planning for community group purchasing for vehicles with different energy sources

To address the issue of final delivery route planning in the community group purchase model, this study takes into full consideration logistics vehicles of different energy types. With the goal of minimizing the sum of vehicle operating costs, delivery timeliness costs, goods loss costs, and carbon emissions costs, a multi-objective optimization model for community group purchase final delivery route planning is constructed. An improved genetic algorithm with a hill-climbing algorithm is utilized to enhance adaptive genetic operators, preventing the algorithm from getting stuck in local optima and improving the solution efficiency. Finally, a case study simulation is conducted to validate the feasibility of the model and algorithm. Experimental results indicate that currently, among the three types of vehicles, fuel logistics vehicles still have an advantage in terms of vehicle usage cost. Electric logistics vehicles exhibit the poorest performance with the highest cost per hundred kilometers, but their sole advantage lies in their high energy release efficiency, enabling optimal low-carbon vehicle performance. Battery-swapping logistics vehicles perform the best in terms of carbon emissions, combining the advantages of both fuel-based and electric logistics vehicles. Therefore, battery-swapping logistics vehicles are a favorable choice for replacing fuel-based logistics vehicles in the future, offering promising prospects for future development.

Study on the Location of Cold Chain Distribution Center Considering Scale Effect

People are paying more attention to the freshness of cold chain products when making choices. Establishing cold chain distribution centers can improve the timeliness and service quality of cold chain logistics. Cold chain distribution centers require significant investment, necessitating a more scientific approach to site selection. Furthermore, compared to regular distribution centers, cold chain distribution centers exhibit clear economies of scale, thus necessitating careful consideration of these economies of scale during site selection and construction. The paper analyzes the scale effect of the cold chain distribution center, and constructs a cold chain distribution center location model considering the scale effect. The objective function of the model includes the related costs of the cold chain distribution center location, such as fixed cost, operating cost, transportation cost, loss cost and carbon emission cost, etc., with the minimum total cost as the function objective. Finally, this paper designs a calculation example design. The model calculation example is solved by Lingo software, and the optimal location plan of the cold chain distribution center is obtained. The calculation example results verify the effectiveness of the model.

Optimizing a Fuzzy Multi-Item Inventory System and Ordering Cost Depletion Contingent on Lead Time With Carbon Emission Cost

Optimizing a Fuzzy Multi-Item Inventory System and Ordering Cost Depletion Contingent on Lead Time With Carbon Emission Cost

Optimal Scheduling of Integrated Energy System Considering Hydrogen Blending Gas and Demand Response

In the context of carbon neutrality and carbon peaking, in order to achieve low carbon emissions and promote the efficient utilization of wind energy, hydrogen energy as an important energy carrier is proposed to mix hydrogen and natural gas to form hydrogen-enriched compressed natural gas (HCNG). It is also injected into the natural gas pipeline network to achieve the transmission and utilization of hydrogen energy. At the same time, the participation of demand response is considered, the load’s peak and trough periods are adjusted, and the large-scale consumption of renewable energy and the reduction in carbon emissions are achieved. First of all, a fine model of hydrogen production and hydrogen use equipment is established to analyze the impact of adding hydrogen mixing on the economy and the low-carbon property of the system. With green certificates and demand response, the utilization rate of hydrogen energy is improved to further explore the energy utilization rate and emission reduction capacity of the system. Secondly, on the basis of modeling, the optimal scheduling strategy is proposed with the sum of energy purchase cost, equipment operation cost, carbon emission cost, wind curtailment cost, and green certificate income as the lowest objective function. Considering the constraints such as hydrogen blending ratio and flexible load ratio of the pipeline network, a low-carbon economic scheduling model of hydrogen mixed natural gas was established. The model was linearized and solved by using MATLAB 2021a and CPLEX solver. By comparing different scenarios, the superiority of the model and the effectiveness of the strategy are verified.