Integrated optimization model for industrial self-generation and load scheduling with tradable carbon emission permits

Reducing electricity cost is important for energy intensive enterprises (EIEs) with self generation power plant, like iron and steel plants. Such an EIE integrates power generation, power consumption and energy storage into a unified whole, and become an enterprise microgrid. Recent methods considering only load scheduling or only power generation scheduling cannot get the minimum total electricity cost. In this paper, an integrated optimization model for generation and batch production load scheduling in EIE is researched to get the minimum electricity cost, without violating constraints of production process. Linearization techniques are utilized to duel with nonlinear factors of the model. Then, mixed integer linear programming (MILP) is used for model solution. The case study of an iron and steel plant shows that the model is effective under a time-of-use (TOU) tariff implemented by the utility.

Climate change, increased carbon regulations, and globalized supply chains are driving industry practitioners and decision makers to implement various carbon policies to reduce carbon emissions. One of the effective approaches to mitigate carbon emissions is the implementation of closed-loop supply chain (CLSC). This paper proposes a deterministic mixed integer linear programming (MILP) model for a multi-period and multi-product closed loop supply chain network with multiple recovery, quality returns and carbon emission considerations. Transportation mode selection decision for logistic activities is also incorporated in the model. Results show that the model captures trade-offs between the total cost and carbon emission. Further, results suggest that carbon price directly effects on the total cost. Conversely, in carbon trading policy, due to having carbon buying and selling flexibility, both total cost and carbon emission are significantly reduced. Sensitivity analysis shows that the operational costs of various recovery activities impact on the total cost. This study provide evidence that besides achieving optimal closed-loop supply chain network design (CLSCND) and planning, it also reduces carbon emissions significantly without increasing the total cost.

Order acceptance and scheduling (OAS) problems are realistic for enterprises. They have to select the appropriate orders according to their capacity limitations and profit consideration, and then complete these orders by their due dates or no later than their deadlines. OAS problems have attracted significant attention in supply chain management. However, there is an issue that has not been studied well. To our best knowledge, no prior research examines the carbon emission cost and the time-of-use electricity cost in the OAS problems. The carbon emission during the on-peak hours is lower than the one in mid-peak and off-peak hours. However, the electricity cost during the on-peak hours is higher than the one during mid-peak and off-peak hours when time-of-use electricity (TOU) tariff is used. There is a trade-off between sustainable scheduling and the electricity cost. To calculate the objective value, a carbon tax and carbon dioxide emission factor are included when we evaluate the carbon emission cost. The objective function is to maximize the total revenue of the accepted orders and then subtract the carbon emission cost and the electricity cost under different time intervals on a single machine with sequence-dependent setup times and release date. This research proposes a mixed-integer linear programming model (MILP) and a relaxation method of MILP model to solve this problem. It is of importance because the OAS problems are practical in industry. This paper could attract the attention of academic researchers as well as the practitioners.

The adoption of closed-loop supply chain (CLSC) network is one of the effective approaches to reduce carbon emissions. In current globalization, inherent uncertainty exists in business environment so there is a need to be design robust supply chains. This paper proposes a deterministic mixed integer linear programming (MILP) model integrating economics and carbon emission considerations including selection of production technologies and transportation mode as a part of CLSC network strategic and tactical decisions. The robust counterpart of the proposed deterministic model is developed based on three alternative uncertainty sets to represent the imprecise input parameters. The robust counterpart is used to study the supply chain performance by considering the two most globally practiced carbon regulatory policies; carbon tax policy and carbon trading policy. Numerical results show that total cost of the proposed robust optimization model under each uncertainty set is greater than the total cost of deterministic model. The additional cost is due to solution space of each uncertainty set to accommodate any uncertainty level. As uncertainty level increases the overall supply chain cost worsen. Moreover, the results suggest that carbon tax rate has direct relation with overall supply chain cost whereas having carbon market trading flexibility in carbon trading policy, this policy is more efficient policy as compared to carbon tax policy. Furthermore, the proposed robust optimization model is useful for mangers to achieve not only a robust supply chain network design which can withstand any possible uncertainty level but also significant reduction in carbon emissions by choosing suitable carbon-efficient policy.

Carbon constraints and carbon emission reduction: An evolutionary game model within the energy-intensive sector

In order to further promote the new energy consumption of combined cooling, heating, and power (CCHP) microgrid, reduce carbon emissions, and optimize the total operating cost of the microgrid, a low-carbon optimal scheduling model of CCHP microgrid was proposed. It considered demand response (DR) and shared energy storage (SES). Firstly, the concept of SES and its operation mode are applied to the microgrid. Secondly, in order to take advantage of the flexible nature of multiple loads, an integrated demand response (IDR) model with a ladder subsidy mechanism was established. Then, the optimal scheduling model introduces a reward and punishment ladder carbon trading mechanism (RPL-CTM). Finally, taking energy purchase cost, load reduction subsidy cost, interaction cost with energy storage station, carbon trading cost, and wind abandoning penalty cost as optimization objectives, a multi-energy coupling, and multi-objective collaborative optimization scheduling model was established. Numerical analysis indicates that the model can efficiently lower the microgrid’s operating cost and encourage the use of clean energy and carbon emission reduction.

ABSTRACTMicrogrids integrated with distributed generation provide energy intensive enterprises (EIEs) with a solution to cut down their total system costs. However, unreasonable capacity allocation of power supply in industrial microgrid may result in capacity shortage or excess, thus leading to uneconomical solutions. Considering carbon emission permits and renewable energy access on demand side, this paper analyses and evaluates the long-term impacts of power supply capacity on economic benefits and carbon emission. The power suppliers consist of industrial self-generation, wind power generation and power grid. A HOMER software based industrial microgrid model is designed, and time series simulation of the model for a cycle of 10 years is performed to provide numerical analysis. The simulation results with sensitivity analysis show that optimal capacity planning of power supply can lead to considerable economical and ecological benefits under carbon emission permits; besides, it can also be conducive to peak load shedding for power grid.

Optimal scheduling of integrated wind- photovoltaic-hydrogen energy system considering hydrogen application and waste heat recovery

An increasing number of firms are integrating carbon emission concerns into their operational decision-making. Some of these actions are motivated by individual initiatives towards corporate environmentalism. While some other are driven by environmental regulation pressures. Policies such as carbon caps under the Kyoto Protocol and carbon tax have been introduced in many countries as mechanisms to induce firms to adopt the low carbon society. In this setting, we present a model for analyzing the impact of carbon emission considerations on the coordination between two/or more different business entities. In the absence of carbon emission considerations, channel coordination has been widely studied in the supply chain management literature. It has been shown that both entities are often better off under the coordinated channel. Several mechanisms and contracts have been then studied to settle down such coordination under different scenarios. The purpose of this research is to investigate these traditional (cost driven) results under carbon emissions considerations. In other terms our objective is to illustrate how the incentives for the coordination are affected by the presence of carbon emission firms' concerns. In our initial analysis we consider traditional buyer-vendor coordination by associating carbon emission parameters with ordering and holding decision variables. We examine how the values of these parameters as well as the considered regulatory emission control policies affect cost and emissions. The corresponding carbon emissions are incorporated into the model through the consideration of various regulatory policies that include : (1) strict emission caps: both of the supplier and the buyer are subject to mandatory caps on the amount of carbon they emit, (2) carbon tax policy: the supplier and the buyer are taxed on the amount of emissions they emit and (3) cap and trade system: the supplier and the buyer are subject to carbon caps but are rewarded /penalized for emitting less/more than their caps. Under carbon emission considerations, the centralized solution remains profit-optimal but not necessarily emission-optimal. This means that the joint profit is often increased when the channel is coordinated. However, the amount of total carbon emissions may be higher than what could be emitted by the retailer and the supplier when they work individually. We identify conditions on cost and emission parameters under which the joint policy is both profit and emission optimal. We also show that the outcome of the coordination is very sensitive to the type of the regulatory policy, for instance some policies are providing greater incentives than others for coordination.

The transition from fossil fuel-powered buses to electric buses has emerged as a central concern in recent years. Governments worldwide are urging this shift in major urban centers as a strategic response to ecological challenges, particularly issues related to pollution and carbon emissions. The primary impediment to the electrification transition lies in real-world financial constraints. Consequently, comprehending both the ecological benefits and financial intricacies of transitioning bus fleets is paramount.In the initial section of this paper, we formulate a mathematical model to assess the reduction in carbon emissions and pollution resulting from a complete transition of the bus fleet. This model is then applied to the city of Sendai, Japan. By comparing the carbon dioxide and emission gases released by conventional buses with those emitted by electric buses, we can discern the impact of employing electric buses on the urban ecological environment.Moving on to the second section, we present a mathematical model delineating the financial aspects of the transition. We use Sendai as a real-world exemplar for our model. The economic model is categorized into expenditures and revenues. Expenditures encompass the acquisition of electric buses, maintenance costs, and carbon taxes. On the income side, we consider bus fares, revenue from bus advertising, the budget of the bus company, and government subsidies. By subtracting expenditures from revenues, we can ascertain the financial ramifications of transitioning to electric buses.Following the evaluation from both ecological and financial perspectives of the bus fleet's electric transition, a third model is constructed mathematically to address the optimal planning solution for a city aiming to fully electrify its bus fleet within a decade. The objective is to achieve cost-efficiency: to be the most economical while meeting ecological goals. Finally, the cities of Sendai, Kunshan, and Nashville serve as subjects for our model, each providing a tailored solution to the transition planning.

This paper presents a bi-level optimization framework for household distributed energy systems (DES), incorporating multiple flexible loads. The upper-level configuration optimization model aims to minimize total system cost, reduce carbon emissions, and maximize renewable energy utilization rate. The lower-level operation optimization model minimizes operational cost while considering multiple flexible loads. This bi-level framework achieves optimal system configuration and efficient operational strategies, enhancing energy efficiency, cost-effectiveness, and environmental performance. This paper also proposes a novel multi-objective optimization method based on Large Language Models (LLM), combined with Mixed Integer Linear Programming (MILP). The impact of flexible loads on DES optimization is evaluated using LLM-MILP and other advanced multi-objective bi-level optimization methods in four scenarios with varying levels of flexible load integration. Furthermore, the performance of these four bi-level optimization methods is assessed based on two metrics: Hypervolume (HV) and running time (RT). The results show that the scenario with fully flexible loads demonstrates notable improvements, with total cost reductions of 20.28%, 16.42%, 13.65%, and 17.43% for MOSFO, MOAHA, NSGA-II, and LLM, respectively. Additionally, carbon emissions decreased by 46.32%, 50.01%, 49.76%, and 49.15%, while renewable energy utilization increased by 1.38%, 22.41%, 25.23%, and 27.56%, compared to the scenario without flexible loads. The proposed LLM-MILP model demonstrates superior computational efficiency, particularly in terms of RT, compared to other bi-level optimization models and can achieve high levels of renewable energy utilization (95.791%), total system cost (933.37 $), and total system carbon emissions (1620.1 t). The seasonal analysis further emphasizes the robustness and adaptability of the optimization framework considering fully flexible loads under varying environmental conditions.

Faced with increasingly strict carbon emission control, high-emission enterprises need scientific and rational management systems and methods to strengthen carbon emission reduction management. Among the many management systems and methods, the carbon budget has become an effective emission reduction management tool, allowing the planning of carbon emissions and emission reduction activities and rational arrangement of economic inputs. However, judging from the research status and business practices in China and abroad, there is no general carbon budget system to guide the development of carbon emission and emission reduction activities. Based on this background, this paper first attempts to construct an enterprise carbon budget system comprising four sub-budgets: carbon emission, carbon emission reduction and cost, carbon emission rights trading, and carbon emission reduction net profit/loss. It draws on the idea of interactive control to consider the impact of changes in carbon prices, energy prices, and policy guidelines on carbon emission reductions and losses. A carbon budget management system based on interactive control is then constructed and applied to China National Aviation Holding Air China Group (AC Aviation). The research results show that the carbon budget system based on interactive control can dynamically adjust carbon emission reduction behavior based on changes in carbon and energy prices to make carbon budgeting a more viable carbon reduction tool and institutional arrangement.

Introduction: To achieve the “dual carbon” goal, the integrated energy system (IES) needs to take into account both economic and low-carbon requirements while meeting the growing energy demand.Methods: Therefore, an optimal scheduling model for low-carbon economic operation is proposed. Firstly, a more accurate carbon emission model is used to consider the actual carbon emission of gas load, to improve the original carbon emission model. A ladder-type carbon trading mechanism is introduced to further constrain the carbon emission of IES. Then, the demand-side response model is proposed, which uses the time-of-use price and mutual substitution of electricity, heat, and gas loads to curtail, time-shift, and substitute the load. Finally, an optimal scheduling model with minimum energy purchase cost, wind and photovoltaic curtailment cost, demand response cost, and carbon emission cost is constructed, which is solved by the GUROBI solver.Results: Through comparative simulation analysis of 6 cases, the results show that the objective function considers the traditional carbon trading cost to reduce carbon emission by about 19.3% compared with the case without considering. After adopting the ladder-type carbon trading mechanism, the carbon emission of IES can be further limited by about 0.35%, and the appropriate carbon trading base price is explored. In addition, after the demand response, the energy purchase cost, carbon trading cost, and carbon emission of IES are reduced by about 3.4%, 18.5%, and 36.2%, respectively, compared with those before the demand response. The simulation results verify the effectiveness of the proposed model.

Vessel routing and optimization for marine debris collection with consideration of carbon cap

This review paper provides the operations management (OM) community with an exhaustive analysis of the mathematical models developed for the problem of low-carbon supply chain management (LCSCM). Our paper belongs to the green supply chain management (GSCM) reviews but is distinguished by its specific interest in analysing research works on supply chain (SC) management regarding the reduction of carbon emissions and its related constraints. To facilitate our benchmarking of the 83 selected papers, we adopt a literature classification based on the logistic decisions studied within the developed models. We distinguish three categories of logistic decisions: operational management, technology investment and SC design coordination. Companies are currently facing great external pressures from governments and their conscientious customers to reduce their overall emissions. We analyse how these environmental constraints, which we believe are key drivers for low-carbon emissions management, have been incorporated into mathematical models. Analysing these external pressures in terms of concern about carbon emissions constitutes our main contribution through this literature review. In addition, companies are facing a challenge to reduce their carbon emissions, which are mainly generated from production, transport and storage activities. Consequently, the modelling of carbon emissions remains a crucial task when addressing the LCSCM problem. We suggest analysing the techniques used thus far to approximate those carbon emissions. Furthermore, to illustrate our literature classification and the features of the LCSCM problem, we provide the framework on which we based our analysis of the selected literature. We discuss the modelling aspects of this problem to highlight the limits of the existing literature and consequently suggest recommendations for future research. We believe that this issue will continue to be one of the top concerns of the OM community within the GSCM field as it continues to gain importance among business leaders, and political and social actors.