Total Operating Cost Research Articles

Optimal Scheduling of the Microgrid Based on the Dynamic Characteristics of the Natural Gas Pipeline Network and the Thermal Network Along with P2G-CCS

In the power system, the integration of power-to-gas and carbon capture systems (P2G-CCS) within the microgrid enables the conversion of electrical energy into hydrogen or methane while simultaneously capturing CO2 emissions from power generation units. This approach significantly mitigates carbon emissions and supports the transition to a low-carbon energy system. Concurrently, the dynamic properties of the gas–thermal network exert a critical influence on the flexibility of system scheduling and the regulation of multi-energy coupling. Hence, this paper puts forward an optimal configuration strategy for microgrids with consideration of the dynamic characteristics of the gas–thermal network. Firstly, mathematical models for the dynamic characteristics of the gas network and the heat network were established and incorporated into the microgrid system. Secondly, in conjunction with the P2G-CCS coupling system, an optimization scheduling strategy was formulated with the aim of minimizing the total operational costs of the power grid, the natural gas network, and the heat network. An enhanced African vultures optimization algorithm (AVOA) was put forward. In the end, by setting different scheduling scenarios for conducting a comparative analysis, an appropriate optimization configuration scheme was selected, and the validity of the proposed method was verified through simulation with the improved case study.

Research on Cross-Border e-Commerce Supply Chain Prediction and Optimization Model Based on Convolutional Neural Network Algorithm

Enhancing the precision of supply chain management and reducing operational costs are crucial for the development of the cross-border e-commerce market. However, existing research often overlooks the demand uncertainty caused by seasonal variations and the challenges of handling returns in logistics. Therefore, this paper proposes a SARIMA-CNN-BiLSTM prediction model that effectively captures both the seasonal and nonlinear characteristics of cross-border e-commerce supply chains. Additionally, by incorporating the returns process, a supply chain distribution optimization model is developed with the objective of minimizing total operational costs. The model is solved using an improved whale optimization algorithm. In validation with real-world data, the SARIMA-CNN-BiLSTM model achieved a mean absolute percentage error reduction of 6.479 and 7.703 compared to convolutional neural network (CNN) and BiLSTM models, respectively. Moreover, the chosen optimization algorithm reduced the cost by 231,310 CNY, 62,564 CNY, and 131,632 CNY compared to the whale optimization algorithm, genetic algorithm, and particle swarm optimization, respectively. The proposed approach provides robust support for cross-border e-commerce enterprises in reducing costs and enhancing efficiency in their operations.

Two-stage multi-objective framework for optimal operation of modern distribution network considering demand response program.

To improve the inadequate reliability of the grid that has led to a worsening energy crisis and environmental issues, comprehensive research on new clean renewable energy and efficient, cost-effective, and eco-friendly energy management technologies is essential. This requires the creation of advanced energy management systems to enhance system reliability and optimize efficiency. Demand-side energy management systems are a superior solution for multiple reasons. Firstly, they empower consumers to actively oversee and regulate their energy consumption, resulting in substantial cost savings by minimizing usage during peak hours and enhancing overall efficiency. The Demand Response Program (DRP) and optimal power sharing have gained significant attention to provide technical and economic benefits, while they require an efficient operation framework. Therefore, a two-stage framework is proposed for multi-objective operation of a distribution network with several generation resources. The first stage applies DRP to maximize the distribution network operator's (DNO) profit by optimizing common incentive rate for all consumers participate in DRP and an individual curtailed power for each consumer. In addition to an individual incentive rate for each consumer participates in DRP which is a new solution in the field of demand side management. The second stage achieves optimal power sharing among generation resources, while considering multiple objectives and incorporating the modified load of the first stage. The multi-objective problem is formulated to reduce energy losses, voltage deviation, total operational cost, gas emissions, and maximize the voltage stability index. The problem is optimized using a combination of the technique for order of preference by similarity to ideal solution (TOPSIS) and the elephant herding optimization (EHO) technique. The framework is validated using a modified IEEE 33-bus that incorporates photovoltaic system, diesel generators, and wind generation system. The proposed framework based on an individual incentive rate DRP provides superior response compared to common incentive rate DRP which reduces the total energy losses by 38.13%, reduces the total generation cost by 9.468%, and reduces the emission by 5.9%.

Optimal operation strategies for freight transport with electric vehicles considering wireless charging lanes

Unlike traditional recharging at fixed stations, wireless charging lanes equipped on the road network enable electric vehicles (EVs) to recharge while in motion, reducing the time required for EV recharging. Indeed, the availability of wireless charging lanes allows the urban freight transport service operators to consider adjusting EVs routing plan to do EV recharging in transit so that the total operational costs can be further reduced. Envisioning that wireless charging lanes would be practically constructed on the road network, this study aims to investigate how the optimal EV routing plan can be determined in a pickup and delivery problem in the future electrified mobility system. The problem is formulated into a mixed-integer programming model, and we propose a branch-and-price algorithm to solve it. Additionally, a large neighborhood search heuristic is incorporated within the column generation framework to effectively tackle the sub-problems. Computational experiments on test instances highlight the effectiveness of our proposed algorithm and demonstrate the potential of wireless charging lanes to reduce total operational costs.

Economical and ecological renewable solution based heating systems for residential building

This paper is an analysis which provides an objective perspective on the performance of different heating sources to facilitate informed decisions regarding investments in thermal energy production equipment that have a low impact on the environment. The aim of the study is to investigate different possibilities to reduce the power consumption and environmental impact taking account annual electricity consumption building for the central-western location in Romania. This paper presented the technical-cost benefit and environmental impact analysis of three alternative heating systems versus conventional systems with natural gas boiler. The obtained results show the higher cost is for ground-to-water heat pump solution and for pellet boiler the price initial investment is higher than that of a gas boiler with 2.15% but is like that of an air-to-water or ground-to-water heat pump. The total operating cost for pellet boiler, air-to-water heat pump and ground-to-water heat pump is higher than natural gas boiler with 65%, 23%, respectively 10%. The amount of CO2 emitted into the atmosphere decreases significantly with solutions air-to-water heat pump and ground-to-water heat pump, with the minimum value of 2502 kg CO2 obtained for the last.

Multiobjective Day‐Ahead Scheduling of Reconfigurable‐Based Microgrids Through Electric Vehicles and Demand Response Integration

Nowadays, as the demand for plug‐in electric vehicles in microgrids is growing, there are various challenges that the network must face, including providing adequate electricity, addressing environmental concerns, and rescheduling the microgrid. In order to overcome these challenges, this paper introduces a novel multiobjective optimization model where the first objective is to minimize the total operation cost of the microgrid and the second objective is to maximize the reliability index by reducing the amount of system energy not supplied. Because of these two compromising objectives, the evolutionary multiobjective seagull optimization algorithm is utilized to find the best local solutions. In this regard, integrated plug‐in electric vehicles and demand response programs are used to smooth distribution locational marginal pricing. Furthermore, the effect of the system’s various configurations is analyzed in the suggested method to smooth the amount of distribution locational marginal prices in comparison to the initial case. Two case studies including modified IEEE 33‐bus and 69‐bus distribution networks are applied to evaluate the efficiency of the proposed approach.

Optimal power scheduling in real-time distribution systems using crow search algorithm for enhanced microgrid performance

Microgrids (MGs) have gained significant attention over the past two decades due to their advantages in service reliability, easy integration of renewable energy sources, high efficiency, and enhanced power quality. In India, low-voltage side customers face significant challenges in terms of power supply continuity and voltage regulation. This paper presents a novel approach for optimal power scheduling in a microgrid, aiming to provide uninterrupted power supply with improved voltage regulation (VR). To address these challenges, a crow search algorithm is developed for effective load scheduling within the distribution system. The proposed method minimizes the total operating cost (TOC) and maximizes VR under varying loading conditions and distributed generation (DG) configurations. A case study in Tamil Nadu, India, is conducted using a microgrid composed of three distributed generation sources (DGs), modeled and simulated using the Electrical Transient Analyzer Program (ETAP) environment. The proposed approach is tested under three operational scenarios: grid-connected mode, islanded mode, and grid-connected mode with one DG outage. Results indicate that the crow search algorithm significantly optimizes load scheduling, leading to a substantial reduction in power loss and enhancement in voltage profiles across all scenarios. The islanded mode operation using the crow search algorithm demonstrates a remarkable reduction in TOC and maximizes voltage regulation compared to other modes. The main contributions of this work include: (1) developing a new meta-heuristic approach for power scheduling in microgrids using the crow search algorithm, (2) achieving optimal power flow and load scheduling to minimize TOC and improve VR, and (3) successfully implementing the proposed methodology in a real-time distribution system using ETAP. The findings showcase the effectiveness of the crow search algorithm in microgrid power management and its potential for application in other real-time power distribution systems.

Optimal Allocation of Community Distributed Energy and Storage Based on Regional Autonomous Balance and Sharing Mechanism

In the context of new power systems, the rapid development of distributed renewable energy and the drive of dual carbon targets have prompted community-level clean energy and energy storage configuration to become the key to improving energy efficiency and reducing carbon emissions. Based on the regional autonomy balance and sharing mechanism, this paper establishes a community distributed energy and energy storage optimization configuration model. With the goal of minimizing the total operating cost of the community, the established model is linearized by using the Big-M method and the McCormick method and transformed into a mixed integer linear programming model that is easy to solve. In order to comprehensively evaluate the comprehensive benefits of the established optimization scheme, this paper introduces the indicators of clean energy self-consumption rate, load self-supply rate, static investment payback period, and static CO2 investment payback period from the aspects of energy utilization, the economy, and the environment. Finally, a calculation example analysis is conducted, and the results show that, compared with the scenario where energy storage is configured separately and distributed energy resources are not shared, the configuration strategy proposed in the article can reduce the energy storage configuration capacity by 46.6% and the distributed energy configuration capacity by 21.1%. Investment costs can be reduced by 15.6%. At the same time, 91.75% of distributed energy self-consumption and 96.80% of load self-supply are achieved, reducing grid interaction and promoting regional autonomy and balance. The static CO2 investment payback period is also significantly shortened, and the carbon emission reduction effect is significant, providing an important reference for community energy system optimization planning and green and low-carbon development.

Electrochemical Mineralization of Chloroquine in a Filter-Press-Type Flow Reactor in Batch Recirculation Mode Equipped with Two Boron-Doped Diamond Electrodes: Parametric Optimization, Total Operating Cost, Phytotoxicity Test, and Life Cycle Assessment

This study investigated the electro-mineralization of chloroquine (CQ) in a filter-press-type flow reactor using two BDD electrodes operating in batch recirculation mode. The optimal operating parameters were established using response surface methodology (RSM) and central composite rotatable design (CCRD) with three parameters: current density (j), initial pH (pH0), and volumetric flow rate (Q), with the mineralization efficiency of (CQ) and specific energy consumption (SEC) as responses. Optimal operating parameters were j = 155.0 mA/cm2, pH0 = 9.75, and Q = 0.84 L/min within a reaction time of 9 h, leading to a maximum mineralization efficiency of CQ of 52.59% and a specific energy consumption of 15.73 kW/mg TOC, with a total operating cost of USD 0.18 per liter. Additionally, an ultra-high-performance chromatography study identified three by-products (4-amino-7-choloroquinoline, formic acid, and acid acetic) of CQ degradation. Furthermore, the phytotoxicity test indicates that the electrochemical wastewater proposed decreased the effluent’s phytotoxicity, and an increase in the percentage of Vigna radiata germination was observed. The carbon footprint of optimized electrochemical mineralization of chloroquine is 2.48 kg CO2 eq., representing a 48% reduction in cumulative energy demand (CED) when the source of energy is a mixture of fossil fuels (50%), wind (25%), and photovoltaic (25%) energy.

Economic Analysis of Red Tilapia (Oreochromis sp.) Production Under Different Solar Energy Alternatives in a Commercial Biofloc System in Colombia

The study investigates the economic aspects of red tilapia (Oreochromis sp.) production using biofloc technology under different electrical energy sources. Conducted at the El Vergel Fish Farming Association in Arauca, Colombia, the study examines four energy treatments: conventional energy (CE), combined conventional and photovoltaic energy (CPVE), full photovoltaic energy (PVE), and simulation of photovoltaic energy generating surplus for nighttime use (PVES). The water quality and zootechnical performance met the species requirements, with dissolved oxygen decreasing as fish size increased. The PVE treatment had the highest initial investment due to solar panels and battery costs, but it also had the lowest operating energy costs. However, the overall costs of the PVE treatment increased due to depreciation and maintenance. Feed was the largest production cost, followed by labor in most treatments, while depreciation was a major cost for the PVE treatment. The total operating cost (TOC) of the photovoltaic energy systems (PVE and PVES) was lower compared to that of conventional energy (CE), with PVES showing the highest cost savings. The reduction in energy costs highlights the potential for solar energy systems to enhance the economic viability of aquaculture production, making these systems a favorable option for sustainable production in the long term.

Optimal configuration strategy of energy storage for enhancing the comprehensive resilience and power quality of distribution networks

Resilience assessment and enhancement in distribution networks primarily focus on the ability to support and recover critical loads after extreme events. With the increasing integration of new energy sources and power electronics, distribution networks have gained a degree of resilience. However, the impact of power quality issues on these networks has become more severe. In some cases, even networks assessed as highly resilient by users suffer equipment damage and substantial economic losses due to power quality issues. To address this issue, this paper builds upon conventional distribution network resilience assessment methods by supplementing and modifying indices in the dimensions of resistance and recovery to account for power quality issues. Furthermore, an optimized energy storage system (ESS) configuration model is proposed as a technical means to minimize the total operational cost of the distribution network while enhancing comprehensive resilience indices. The proposed nonlinear optimization model is solved using second-order cone relaxation techniques. Finally, the proposed strategy is simulated on the IEEE 33-node distribution network. The simulation results demonstrate that the proposed strategy effectively improves the comprehensive resilience indices of the distribution network and reduces the total operational cost.

Assessing the Impact of Surface Blast Design Parameters on the Performance of a Comminution Circuit Processing a Copper-Bearing Ore

Open-pit mining remains the dominant method for copper extraction in current operations, with blasting playing a pivotal role in the efficiency of downstream processes such as loading, hauling, crushing, and milling. This study assesses the impact of surface blast design parameters on the performance of a comminution circuit processing a copper-bearing ore. The analysis focuses on important design parameters such as burden, spacing, stemming, and powder factor, evaluating their influence on the fragment size distribution and downstream comminution circuit performance. Using the Kuz-Ram model, four novel blast designs are compared against a baseline to predict the size distribution of rock fragments (X80). Key performance indicators throughput and specific energy consumption are calculated to evaluate the comminution circuit performance. Results demonstrated that reducing the X80 from 500 mm to 120 mm led up to a 20% increase in throughput and a 29% reduction in total specific energy consumption. Furthermore, achieving finer particle sizes through more intensive blasting contributed to a reduction in total operating costs by up to 12%. These findings provide valuable insights for optimizing blast design to improve comminution circuit performance, contributing to sustainable mining practices by reducing energy consumption, operating costs, and the environmental footprint of mining operations.

H2-powered aviation – Optimized aircraft and green LH2 supply in air transport networks

The final financial decisions on starting the commercialization of the next single-aisle aircraft programs for entry-into-service in the 2030s are due in less than 5 years. These programs will shape the future climate impact over the following 20–30 years of this aircraft segment and will determine if the sector can achieve its 2050 net-zero target. And so far, there are only limited holistic research perspectives available evaluating the best decarbonization options for such a crucial next product.This study provides a first-of-its-kind holistic evaluation approach for the business case of single-aisle hydrogen-(H2)-powered aircraft to enable true-zero CO2 flying. It combines the optimization of green liquid hydrogen (LH2) supply and aircraft designs as well as the investigation of operational strategies with such aircraft in one specific air traffic network.It is found that LH2 could cost around 2 to 3 USD/kg at main European airports in a 2050 scenario. Even though the aircraft with H2 direct combustion would be less efficient, average total operating costs would be 3% lower than flying with synthetic kerosene in the given network in 2050. As an operational strategy to save fuel costs, tankering might play an essential role in reducing operating costs for H2-powered aircraft in the early adoption phase with high differences in LH2 supply costs.Finally, it is derived that usage of LH2 as a fuel would lead to lower installation requirements of renewable energy generation capacity compared to the synthetic kerosene option. Since green electricity will be a constrained resource in the next decades, this is another important aspect for choosing future decarbonization options in air travel.All in all, the study proves the importance of the derived methodology leading to a broader techno-economic assessment for two decarbonization options in aviation. Such novel approaches might be further developed and applied to other related research topics in this field.

Exploiting geospatial shifting flexibility of building energy use for urban multi-energy system operation

As one of the major energy consumers and greenhouse gas emitters, cities are at the center of global energy transition. For city managers, the energy consumption in the building sector is an important flexibility resource that can be utilized to improve the operation of urban multi-energy systems. Although demand response strategies have been widely studied for the temporal shifting of building energy use, the potential of spatial shifting flexibility is still lack of exploration. To this end, this paper proposes a novel approach to estimate and utilize the geospatial load shifting flexibility at the city scale. At the estimation stage, a methods is developed to calculate urban building energy consumption using publicly available databases and simulation tools, and the spatial distribution of supply side (energy infrastructures) and demand side (buildings) is analyzed through geographical information system. In the utilization part, a novel optimal energy flow model is proposed, where the geospatial shifting of load demand among different energy suppliers is achieved by topology reconstruction and equipment switching. To demonstrate the effectiveness and superiority of the proposed method, a real-world case study is conducted. The results show that the proposed approach can effectively exploit the spatial shifting flexibility of load demand, and the operation indicators of urban multi-energy systems such as total operating cost and maximum available capacity have also been improved.

Environmental and economic assessment of urban wastewater reclamation from ultrafiltration membrane-based tertiary treatment: Effect of seasonal dynamic

This study aimed to assess the environmental and economic performance of an ultrafiltration (UF) tertiary treatment of effluent from an urban wastewater treatment facility. Data from a UF demonstration plant composed of commercially available equipment, including industrial hollow-fiber membranes was used to project a full-scale facility. The results from the demonstration plant recommended different ranges of transmembrane fluxes and sparging air demands under summer and winter conditions to prevent excessive fouling. The energy balance of the full-scale facility would be 0.308 ± 0.112 kWh·m−3 in summer and 0.140 ± 0.040 kWh·m−3 in winter, with blowers' being the main consumers (86–93 %). CAPEX accounted for €0.030 ± 0.002·m−3 in summer and €0.027 ± 0.002·m−3 in winter and membrane acquisition represented 66–69 % of the investment cost. Energy expenditure was the major OPEX cost (66–79 %), with a total operating cost of €0.077 ± 0.023·m−3 and €0.042 ± 0.008·m−3 in summer and winter, respectively. The final average value obtained for the TAC was €0.107 m−3 in summer and €0.068 m−3 in winter. The environmental assessment confirmed optimizing energy consumption and membrane requirements as the main factors influencing environmental sustainability. Specifically, summer and winter emissions of 0.079–0.175 and 0.043–0.079 kgCO2eq·m−3 (Global warming potential); 8.1 · 10−4–1.7 · 10−3 and 4.8 · 10−3–8.1 · 10−3 m3·m−3 (water consumption); 0.019–0.041 and 0.010–0.019 kg oileq·m−3 (fossil fuel scarcity); and 1.4 · 10−4–2.9 · 10−4 and 7.7 · 10−4–1.4 · 10−4 kg Cueq·m−3 (mineral resource scarcity) were calculated, respectively. The obtained permeate quality complied with the most stringent Spanish and EU regulations.

Exact solution of location–routing problems with heterogeneous fleet and weight-based carbon emissions

This paper investigates a location–routing problem with heterogeneous fleet and weight-based carbon emissions, aiming to minimize total operating costs, including depot and vehicle fixed costs and carbon emissions. The problem is formulated using an extended formulation, from which a relaxation is derived. The relaxation is further strengthened with additional cuts. For this problem, we devise an exact algorithm that combines a branch-price-and-cut algorithm with state-of-the-art technologies. The exact algorithm relies on a novel bidirectional labeling algorithm; it merges labels through cost compensation, leading to an effective solution. We conduct comprehensive computational experiments to validate the proposed algorithm, evaluating key factors related to carbon emissions. Our findings confirm the algorithm’s efficacy, highlighting its potential as a useful tool for addressing the location routing problem in heterogeneous fleet scenarios.

Selective Leaching for the Recycling of Lithium, Iron, and Phosphorous from Lithium-Ion Battery Cathodes’ Production Scraps

The market for lithium iron phosphate (LFP) batteries is projected to grow in the near future. However, recycling methods targeting LFP batteries, especially production scraps, are still underdeveloped. This study investigated the extraction of iron phosphate and lithium from LFP production scraps using selective leaching, considering technical and economic aspects. Two leaching agents, sulfuric acid (0.25–0.5 M, 25 °C, 1 h, 50 g/L) and citric acid (0.25–0.5 M, 25 °C, 1 h, 70 g/L) were compared; hydrogen peroxide (3–6%vv.) was added to prevent iron and phosphorous solubilization. Sulfuric acid leached up to 98% of Li and recovered up to 98% of Fe and P in the solid residues. Citric acid leached 18–26% of Li and recovered 98% of Fe and P. Totally, 28% of Li was precipitated for sulfuric acid process, while recovery with citric acid did not produce enough precipitate for a characterization. Sulfur is the main impurity present in the precipitates. The total operative costs associated with reagents and energy consumption of the sulfuric acid route were below 3.00 €/kg. In conclusion, selective leaching provided a viable and economic method to recycle LFP production scraps, and it is worth further research to optimize Lithium recovery.

Multi-objective DG placement in radial distribution systems using the IbI logic algorithm

This paper presents a unique optimization method based on the incomprehensible but intelligible-in-time (IbI) logic algorithm (ILA) to optimally place dispersed generators in small, medium, large, and very large (16-, 33-, 69-, and 118-bus) radial distribution power networks to reduce power losses, the total operating cost, and the voltage deviation and improve the voltage level. Two types of multiple distributed generators (DGs) are employed in this study, one working at unity power factor and the other at 0.866 p.f. The IbI logic algorithm works by understanding concepts that are not currently recognized as logical but are expected to become logical over time. The proposed approach was used to address a multi-objective multi-DG placement problem. The results generated through this method were compared with those generated by other methods and were observed to be comparatively remarkable.

The green marine waste collector routing optimization with puma selection-based neighborhood search algorithm

Improper waste disposal by humans has created significant environmental issues in the marine ecosystem, including endangering aquatic life and accelerating the extinction of certain marine species. Due to the floating nature of the marine debris, the coordinates for collecting activities must be estimated in advance. In this article, GNOME software is used to estimate the coordinates of debris, and then a fleet of several ships is used to collect them. Also, a mixed integer linear programming model is presented for the routing optimization of debris collection fleets. The proposed optimization model formulates the objective function based on numerous factors, including labor cost, rent, and ship insurance, and considers constraints on fuel tank capacity, the time window, and the ship’s cargo capacity. A new hybrid algorithm combining the Puma algorithm and neighborhood search is proposed to address the problem. Metropolis acceptance is used in the simulated annealing algorithm to avoid the local optima and greedy selection. Numerical examples of the marine survey and the port of Rotterdam are used to test the proposed approach, which has been proven effective in several scenarios. Results achieved from the proposed hybrid method demonstrate considerable performance improvement in solving the problem. This approach has decreased total fuel and labor costs by 10–15% compared to conventional methods, with minimized time window violation reaching 25%. These results show a significant reduction in total operational costs with proper scheduling and route planning.

Joint peak power and carbon emission shaving in active distribution systems using carbon emission flow-based deep reinforcement learning

Distribution optimal power flow (D-OPF) with peak load shaving function is crucial for guaranteeing economical and reliable operations of active distribution grids with various distributed energy resources. However, conventional D-OPF methods reduce only the power operation cost without considering carbon emission reduction, which may lead to a slowdown in achieving global carbon neutrality. To resolve this issue, this study proposes a deep reinforcement learning (DRL)-assisted D-OPF framework realizing dual-peak shaving of power and carbon emission for low-carbon active distribution system operations based on the notion of carbon emission flow (CEF). The proposed framework aims to minimize the total power operation costs of substation and gas-turbine (GT) generators. It also aims to reduce the total carbon emission cost via mitigation of peak power and carbon emission in the CEF-based D-OPF framework with both power and carbon emission peak constraints. A key feature of the proposed framework is the adoption of the DRL method for the CEF-based D-OPF problem to determine economical and eco-friendly peaks of power and carbon emission under dynamically changing distribution system operations. Furthermore, a D-OPF optimization-based reward function for the DRL agent is designed to yield no constraint violations for the D-OPF problem during the agent’s training phase. Numerical examples conducted on the IEEE 33-node and IEEE 69-node distribution systems with GT generators, solar photovoltaic systems, and energy storage systems demonstrate that, in contrast with CEF-free and CEF-integrated optimization methods with fixed power and/or carbon emission peaks, the proposed method further reduces the total carbon emission and cost.