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In response to the challenges of global warming and the development of A low-carbon economy, the integrated electricity and natural gas energy system (IEGES) is known as an important structure for future energy supply; thus, its planning and design must take low-carbon and environmental protection factors into account. Regarding carbon emissions as an optimization criterion, this paper built life-cycle carbon emission models of IEGES components. Then, taking the capacities of the energy resources, storage and conversion units of IEGES as the optimization variables, a multi-objective optimization configuration model was established considering the annual investment operation cost and the life-cycle carbon emissions. The multi-objective model was transformed into a single-objective one by an ε-constraint approach and the polynomial fitting method was employed to obtain the value of ε for obtaining uniformly distributed Pareto sets. Based on the fuzzy entropy weight method and the fuzzy affiliation degree approach, the obtained Pareto sets were ranked and the solution with the highest ranking value was selected as the optimal solution for the original problem. Finally, the configuration schemes were analyzed from the perspectives of economy, carbon emission and renewable energy utilization, and the effectiveness and rationality of the proposed optimization method were verified through MATLAB simulation. [ABSTRACT FROM AUTHOR]

The carbon neutrality goal places greater demands on integrated energy system (IES) in rural areas. In line with this trend, a multi-objective stochastic-robust based selection-allocation-operation cooperative optimization model is established for planning of rural IES in this paper. Firstly, this paper construct three typical IESs at three levels (user, township and user-township interaction) and make economic selection. Secondly, in order to coordinate the objectives of technology-economy-environment and solve the multiple uncertainties existing in the system, a multi-objective stochastic-robust planning model is proposed. In the model, the objective function introduces indicators such as total cost, primary energy resource efficiency and renewable energy consumption rate. Then constructing stochastic hierarchical scenarios to characterize the stochastic uncertainty of parameters in the time dimension. And introducing typical scenarios into the box sets which characterize the supply-demand sides uncertainty. Finally, a sensitivity analysis is performed to discuss the influence of robustness parameters and biogas prices. The annual total cost of the user-township interaction system is 53.8 % and 8.4 % lower than that of the other two systems, respectively. And from the allocation results of selected system, primary energy resource efficiency is 0.57, total cost is 1.81 × 106 USD and renewable energy consumption rate is 99.46 %. In addition, from the operation results, it is analyzed that the output of the equipment is perfectly balanced with the user's load. Finally, it is obtained by sensitivity analysis that uncertainty budget and biogas price have an impact on the selection of discrete equipment, the capacity of continuous equipment and the total cost. And the optimal capacity of the system under different situations is given. [ABSTRACT FROM AUTHOR]

The application of sustainable manufacturing technologies is the new challenge faced by enterprises, industries, and researchers under the background of supporting carbon peak and carbon neutral. This paper studies how to reduce the energy consumption of hole machining through optimizing tool path and cutting parameters simultaneously. The integrated optimization methodology can further reduce the energy consumption comparing with optimizing the tool path or cutting parameters separately. Firstly, the energy model of hole machining is established based on machine tools' energy composition, tool path planning, and process parameters. Due to the fact that tool path planning as air cutting process has big relationship with reducing energy, especially for hole group with a big proportion, the tool path of hole processing is optimized by the improved ant colony algorithm to solve the issue considering the distance from one hole to the next hole. Based on this optimized path, a multi-objective optimization model for one hole cutting parameters is established, considering the spindle speed and feed rate as the optimization variables and machining time, energy consumption, and surface roughness as the objective function. The non-dominated sorting genetic algorithm (NSGA-II) is employed to solve the multi-objective optimization problem of hole machining. Finally, the case study with 50 holes is used to testify the application of the proposed method to provide the practical energy efficiency strategy for hole group or multi-hole parts on CNC machines assisting in achieving sustainable production in manufacturing sectors. [ABSTRACT FROM AUTHOR]

Biomass direct-fired power plant integrated with carbon capture system can achieve negative CO 2 emission during the power generation, which provides an important technology pathway for the transformation of energy system towards carbon neutrality. Understanding the economic competitiveness and CO 2 reduction potential of the plant, and identifying the optimal operating mode are the key to fully exert the advantages of the technology. For this reason, this paper develops life cycle economic and CO 2 emission assessment models for the biomass direct-fired circulating fluidized bed boiler power plant (BCFBP) integrated with solvent-based post-combustion carbon capture (PCC) system. The levelized cost and CO 2 emission of energy indexes are then applied to evaluate the performance of the BCFBP-PCC system. Based on the life cycle assessment, a novel economy and CO 2 reduction dual-objective operating optimization approach is proposed for the BCFBP-PCC to find the optimal hourly power generation and CO 2 capture level of the plant under given market conditions. The static investment payback period is employed as a constraint in the optimization to narrow down the range of the solution set and provide clearer guidance for system operation. This paper provides the first work pioneered the economic and negative carbon operation optimization of the BCFBP-PCC system. [ABSTRACT FROM AUTHOR]

In the background of exhaustion of the traditional fossil energy sources and environmental deterioration, developing renewable energy has become a strategic choice for countries to achieve energy sustainable utilization and carbon neutrality target. Different renewable energy technical plans have different characteristics under multiple criteria. Therefore, before the further exploitation of renewable energy sources, it is of great significance to evaluate the comprehensive performance of different plans and then determine the best renewable energy sources. However, the commonly used weight calculation methods in the existing renewable energy evaluation have obvious shortcomings, such as the randomness of the subjective method is strong and the gap in the weight allocation of the objective method is too large, which affect the reliability and accuracy of the sorting result. In this paper, a novel weight calculation method based on non-dominated sorting genetic algorithm (NSGA-II) is proposed, and is applied to renewable energy evaluation. The study in this paper focuses on methodology. Firstly, for the linear normalized and Zeros normalized evaluation data, the differences between renewable energy plans in the two normalized evaluation data are measured, and the ranking deviations of the two normalized data are calculated. Then, the multi-objective weight solving model is constructed in terms of plans differences minimization and ranking vectors deviations minimization. The NSGA-II algorithm is applied to the optimization of multi-objective functions to find the Pareto non-dominated solution, so as to determine the weight values of each evaluation index. Finally, the TOPSIS method is used to determine the relative closeness value of each plan, and the rankings of renewable energy technical plans are achieved. Based on the actual renewable energy development data of a province in China, experiments were carried out to investigate the effectiveness of the proposed method. Experimental results show that the proposed method performs better than some popular renewable energy evaluation methods. [Display omitted] • Propose a novel evaluation method for renewable energy technical plans. • Apply non-dominated sorting genetic algorithm II to determine the index weights. • Compared with the existing evaluation method, the proposed method performs better. [ABSTRACT FROM AUTHOR]

Hydrogen production by electricity is one of the effective means of large-scale use of renewable energy, and it is also the inevitable direction of the green and low-carbon development of the hydrogen energy industry. However, the existing studies have not deeply considered the operation of electricity-hydrogen coupling system (EHCS) in multiple markets, such as electricity, carbon, hydrogen, and auxiliary service, and have not fully explored the value of EHCS in terms of economy, environment, and other aspects, and there are still deficiencies in dealing with the uncertainty of EHCS operation. To this end, this paper innovatively proposes a two-stage multi-objective distributionally robust optimization (DRO) model for the EHCS under the coupling of electricity, carbon, hydrogen, and standby auxiliary markets. The first stage is a multi-objective optimization model based on the wind turbine (WT) and photovoltaic (PV) predicted power counting economy, energy supply stability, and users' energy use comfort, which determines the feasible domain of planned power; the second stage is a multi-objective DRO model based on the prediction deviation of WT and PV power, which smoothes out the fluctuation of power. Different scenarios are designed for numerical analyses to verify the validity of the model and the superiority of the simultaneous participation of EHCS in multi-coupled markets. The simulation results show that: 1) Simultaneous participation of the EHCS in various coupled markets such as electricity, carbon, hydrogen, and standby auxiliary service can significantly improve economic efficiency. Compared with only participating in the electricity and hydrogen markets, the simultaneous participation of EHCS in the standby auxiliary service and carbon trading markets increases the economic efficiency by 141.36 %. 2) Under multi-objective optimization, although the EHCS sacrifices some economic benefits, it can improve the stability of energy supply and the comfort of users' energy use. Compared with the pursuit of economic benefits only, under multi-objective optimization, the equivalent load variance of the EHCS is reduced by 45.77 %, and the discomfort of users' energy use is reduced by 55.34 %, which is more conducive to long-term development. 3) The DRO method has significant advantages in dealing with uncertainty, overcoming the drawbacks of the stochastic optimization (SO) method's low solving efficiency and poor robustness, and the robust optimization (RO) method's over-conservative solving results. • The operation situation for EHCS under multiple markets is considered. • A multi-objective operation optimization model for EHCS is constructed. • A two-stage DRO model for EHCS operation is proposed. [ABSTRACT FROM AUTHOR]

Employing multi-energy complementarity in project energy supply proves to be an efficient strategy for accomplishing low-carbon project scheduling. However, current project scheduling problems primarily concentrate on traditional single-energy models. They optimize schedules to reduce energy emissions but often neglect the potential of renewable energy. This paper integrates project scheduling and renewable energy as interrelated aspects. Based on a renewable energy supply model grounded in the generation characteristics of renewable energy sources, a model for MC-MRCPSPM (multi-mode resource-constrained project scheduling problem with multiple complementary energies in the manufacturing context) is constructed and a memetic algorithm is employed for its resolution. The research findings conclusively demonstrate that using renewable energy as a power source during project execution leads to an average reduction of approximately 58.8% in carbon emissions, with a justifiable 14.73% increase in energy costs. Employing a multi-energy complementarity approach in project scheduling effectively reduces carbon emissions while maintaining reasonable energy costs and offers project managers greater decision-making flexibility. [Display omitted] • Integrating renewable energy in project scheduling achieves low-carbon goals. • A model integrating renewable and conventional energy sources was proposed. • Proposing a memetic algorithm efficiently addresses the problem. • Multi-energy supply can reduce emissions, with slight cost increase. • References for optimizing cost-carbon balance in project management decisions. [ABSTRACT FROM AUTHOR]

• BWO-FLANN model has been proposed for weight updation in the FLANN network. • NS-MOTLBO is proposed for solving the EELD issue in the predicted load condition. • One constraint handling approach is used for generating the Pareto solution. In this paper, the future load condition is predicted by proposing an effective single-layer Black Widow Optimization based Functional Link Artificial Neural Network (BWO-FLANN) model. Also, a novel Non-dominated Sorting Multi-Objective Teaching Learning Based Optimization (NS-MOTLBO) has been proposed for obtaining the solution to Economic Emission Load Dispatch (EELD) in a combined operation of the grid with solar photovoltaic source by considering the future predicted load. The EELD is an inherently multi-objective problem with nonlinear constraints, such as the generator power limits, power balance criteria, transmission line capacity, prohibited operating zones, etc., that make it more complex, causing extra computational burden and convergence issues. An effective constraint-handling methodology is also used in this paper to achieve the lowest possible constraint violation. The proposed approach has been implemented in two test situations, each with distinct solar radiation. The Pareto optimal fronts for various hours were obtained, indicating possible scheduling of the corresponding periods. Non-parametric statistical testing is carried out to investigate the dominance of our proposed approach compared to the other state-of-the-art prediction models. [ABSTRACT FROM AUTHOR]

Integrated energy system (IES) plays a vital role in achieving energy revolution and the goals of carbon peak and carbon neutrality. The optimal planning of IES is of great significance for improving the overall efficiency of the system and promoting its sustainable development. Focusing on this issue, this paper proposes a planning framework integrating multi-objective optimization with fuzzy multi-criteria decision making (MCDM). In this framework, IES planning is modeled as a multi-objective optimization problem that, for the first time, simultaneously minimizes energy consumption, carbon emissions, and economic costs. Thereafter, the optimization problem is solved by a multi-objective state transition algorithm based on decomposition (MOSTA/D), which generates a Pareto set that realizes multiple conflicting objective tradeoffs. Furthermore, to comprehensively evaluate the Pareto optimal solutions, an evaluation criteria system is established from various perspectives, and a novel MCDM approach is proposed. This approach combines the analytic network process-entropy weighting technique, which takes into account the correlation between criteria as well as subjective preference and objective information, with fuzzy TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) for scientifically ranking and selecting solutions under uncertainty. The simulation results of an IES planning case study demonstrate that the optimal scheme determined by the proposed method achieves the best overall benefit for IES, with significant annual economic cost savings, primary energy savings, and carbon dioxide emission reduction rates of 2.27%, 40.36%, and 56.25%, respectively, proving the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

It is essential to reduce carbon emissions in wastewater treatment plants (WWTPs) to achieve carbon neutrality in society. However, current optimization of WWTPs prioritizes the operation cost index (OCI) and effluent quality index (EQI) over greenhouse gas (GHG) emissions. This study aims to conduct a multi-objective optimization of a WWTP, considering GHG emissions, EQI, and OCI. The anaerobic-anoxic-oxic integrated membrane bioreactor (AAO-MBR) process in an actual WWTP was selected as a typical case, tens of thousands of scenarios with combinations of six operational parameters (dissolved oxygen (DO), external carbon resource (ECR), poly aluminum chloride (PAC), internal reflux ratio (IRR), external reflux ratio (ERR), and sludge discharge (SD)) were simulated by GPS-X software (Hydromantics 8.0.1). It was shown that ECR has the greatest impact on optimization objectives. In the optimal scenario, the main parameters of ATDO, MTDO, IRR, and ERR were 0.1 mg/L, 4 mg/L, 50%, and 100%, respectively. The EQI, OCI, and GHG of the best scenario were 0.046 kg/m3, 0.27 ¥/m3, and 0.51 kgCO2/m3, which were 2.1%, 72.2%, and 34.6% better than the current situation of the case WWTP, respectively. This study provides an effective method for realizing low-carbon and economical operation of WWTPs. [ABSTRACT FROM AUTHOR]

• Based on the energy hub, the architecture of the park-level integrated energy system is constructed considering the coupling and conversion between multiple energy sources and the demand response on the user side. • A multi-objective robust model and opportunity model based on IGDT are established from the perspectives of risk aversion and risk preference, respectively. • Compared with the traditional carbon trading mechanism, the introduction of the ladder-type carbon trading mechanism can significantly reduce carbon emissions. In order to meet the challenge of global low-carbon development, the concept of integrated energy system (IES) has been proposed and demonstrated, and its low-carbon operation mode is the research hotspot at present. In this paper, a multi-objective optimal scheduling model considering the participation of park-level IES (PIES) in the carbon market is proposed, which takes into account the multiple uncertainties on the renewable energy and load. Firstly, for the PIES operator (PIESO) participate in the carbon market, this paper introduces the ladder-type carbon trading mechanism. Then, for the uncertainties of the renewable energy and load, this paper carries out uncertainty modeling from the perspective of PIESO risk aversion and risk preference based on IGDT method. Finally, a multi-objective optimal scheduling model including economic and environmental objective is established. The actual PIES is selected for case study, and the results show that the operating profit of PIESO under the risk aversion strategy decreases by 5.91%, the operating profit of PIESO under the risk seeking strategy increases by 6.12%. Aiming at the impact of ladder-type carbon trading on system operation profits and carbon emissions, three scenarios were set, including whether to introduce a ladder-type carbon trading mechanism, changes in the initial price and the tradable carbon emission ratio. The results show that compared with the traditional carbon trading mechanism, the introduction of the ladder-type carbon trading mechanism reduces carbon emissions by 2.73%, and the setting of reasonable ladder-type carbon trading price and tradable carbon emission ratio can significantly reduce the carbon emissions, improve operating profit of PIES. [ABSTRACT FROM AUTHOR]

Carbon trading prices are crucial for carbon emissions and transparent carbon market pricing. Previous studies mainly focused on data mining in the prediction direction to quantify carbon trading prices. Although the prospect of high-frequency data forecasting mechanisms is considerable, more mixed-frequency ensemble forecasting is needed for carbon trading prices. Therefore, this article designs a new type of ensemble prediction model to increase the scope of model research. The module is divided into three parts: data denoising, mixed frequency and machine learning, multi-objective optimization, and ensemble forecasting. Precisely, the data preprocessing technology enhanced by adopting a self-attention mechanism can better remove noise and extract effective features. Furthermore, mixed frequency technology is introduced into the machine learning model to achieve more comprehensive and efficient prediction, and a new evaluation criterion is proposed to measure the optimal submodel. Finally, the ensemble model based on deep learning strategy can effectively integrate the advantages of high-frequency and low-frequency data in complex datasets. At the same time, a new multi-objective optimization algorithm is proposed to optimize the parameters of the ensemble model, significantly improving the predictive ability of the integrated module. The results of four experiments and the Mean Absolute Percent Error index of the proposed model improved by 28.3526% compared to machine learning models, indicating that the ensemble model established can effectively address the time distribution characteristics and uncertainty issues predicted by carbon trading price models, which helps to mitigate climate change and develop a low- carbon economy. [ABSTRACT FROM AUTHOR]

In the context of carbon neutrality, renewable energy, especially wind power, solar PV and hydropower, will become the most important power sources in the future low-carbon power system. Since wind power and solar PV are specifically intermittent and space-heterogeneity, an assessment of renewable energy potential considering the variability of wind power and solar PV with high temporal resolution in different regions will facilitate more accurate identification of the decarbonization pathway of power system. In this paper, the complementary output potential of wind-solar-hydro power every 15 min in 31 Chinese provinces is evaluated by developing a multi-objective optimization model based on Nondominated Sorting Genetic Algorithm II. The results show that the total annual complementary power generation potential in China is 17.57 PWh, of which the three components account for 47.8%, 25.3% and 26.9%, respectively. Inner Mongolia and Tibet are the major renewable energy source provinces with annual complementary power generation potential of 5330 TWh and 3907 TWh, accounting for 30.3% and 13.7% of the total power generation potential, respectively. The temporal potential of wind-solar-hydro power varies greatly, with daily potential is more volatile than monthly. Seasonal and spatial heterogeneity of the complemental renewable potential makes some provinces suffer power shortage during long-hours period especially in winter. Our findings will encourage a higher penetration of renewable energy, the promotion of multi-energy complementarity, and the development of inter-provincial power transmission and energy storage infrastructure in China's future power sector. • A multi-objective wind-solar-hydro complementary optimization model is developed. • Electricity supply and demand have gaps at different temporal and spatial scales. • Wind-solar-hydro complementary potential shows great temporal and spatial variation. • Renewable complementarity can improve China's future power system stability. [ABSTRACT FROM AUTHOR]

Given the ladder-type carbon tax in the carbon quota market is a relatively macroscopic large-time scale concept, and the volatility and uncertainty of new energy generation output and the load, it is currently difficult to accurately predict and control carbon trading cost in the multi-time scale scheduling of integrated energy system (IES), there is a lack of in-depth research on dynamic adjustment and ground implementation in small time scale. To address the challenges of these problems, this paper proposes a multi-objective optimal scheduling model for IES considering the multi-timescale ladder-type carbon trading mechanism. Optimization models and algorithms were developed for day-ahead, intraday and real-time scheduling time scales, enhancing scheduling accuracy and usability. Day-ahead scheduling minimizes comprehensive cost and pollutant emissions while considering the ladder-type carbon trading mechanism; intraday scheduling dynamically adjusts carbon trading cost functions based on day-ahead results and equipment output; and real-time scheduling establishes new carbon trading ladder-type cost functions according to intraday results and emissions. A novel particle swarm optimization algorithm was introduced, integrating uncertainty models for wind, photovoltaic power, and load, along with electric vehicle charging strategies. The multi-time scale model in ladder-type carbon trading exhibits approximately 5% more economic advantage and 2% more environmental benefit. • Developed IES model with LTCTM for improved carbon trading cost prediction. • Proposed an improved Particle Swarm Optimization for multi-objective scheduling. • Constructed uncertainty models with EV strategies for robust scheduling process. • Demonstrated ladder-type carbon trading advantages through a case study. [ABSTRACT FROM AUTHOR]

District heating is an effective way to improve energy efficiency and decrease emissions. In studies, district heating multi-objective optimization only focuses on the fourth-generation system's planning but ignores the operation optimization of the third-generation. The exergy-related multi-objective optimization is usually combined with economy, while the environmental aspects, especially carbon emissions trading is rarely introduced. Auxiliaries consume lots of energy but are seldom considered both in modeling and optimization. Thus, this paper develops a multi-objective optimization model integrating exergy, environment, and economy with careful consideration of auxiliary equipment modeling and carbon emission trading. The model is validated by a special third-generation district heating system with turbine-driving fans and pumps located in Shenyang, Northeast China. The non-dominated sorting genetic algorithm is used to solve the model. No power generation, self-use priority, and on-grid priority are optimized and the exergy efficiency is improved by about 3–4%. Variable speed of auxiliary equipment can reduce power consumption in the cubic form of the variable speed ratio's reciprocal, and four times the carbon trading price of the current can lead to a one-third proportion in expenditure. The outcomes can be referred to make energy policy and operation decisions related to district heating. • A district heating system featuring turbine-driving fans and pumps is optimized. • Environmental effects are considered in the form of carbon emission trading. • A detailed auxiliary equipment model and variable speed regulation are included. • No power generation, self-use priority, and on-grid priority are compared. [ABSTRACT FROM AUTHOR]

Carbon neutrality is one of the key issues in mitigating global climate change. The carbon neutrality process accelerates the global energy transformation from fossil energy to renewable energy. Among the main indicators related to carbon neutrality proposed by the Chinese government, those directly related to energy include carbon emission intensity, the proportion of nonfossil energy consumption, and the total installed capacity of wind and solar power generation. It can be seen that the transformation of energy development is crucial for realizing carbon neutrality. Meanwhile, the correlation between carbon neutrality and economic development is not antagonistic. On the contrary, carbon neutrality can not only enhance the quality and efficiency of traditional industries but also improve the overall development of society. Therefore, the coordinated and sustainable developments of the energy - economy-environment (3E) system is the priority for achieving the target of carbon neutrality. However, the impact of carbon neutrality on regional development requires long-term consideration, which increases the difficulties of comprehensive planning. In this study, a long-term comprehensive programming model of regional energy and economic development under carbon peaking and carbon neutrality targets was established. A carbon neutrality assessment indicator (CNAI) framework was constructed to assess the effect of carbon neutrality actions in different regions. Meanwhile, a LEAP model was developed to simulate energy demand and carbon emissions. The multi-target weights determined by assessment results of CNAI and the parameters range set by LEAP model were the basis of the multi-objective optimization model to support regional development. Furthermore, the proposed method was applied to Shaanxi province in China, which offered quantitative targets and suggestions for energy structure transformation and industrial structure adjustment. The results show that coal and oil will account for 6.25% of the total energy consumption and the proportion of secondary industry will drop to 25.40% by 2060. The method established in this study provides a new scientific approach for resolving multi-objective trade-offs in long-term programming and determining a reasonable optimization range for regional development under climate mitigation policies. [ABSTRACT FROM AUTHOR]

Against the background of carbon neutrality, the power dispatching operation mode has undergone great changes. It not only gradually realizes the coordinated control of source–grid–load–storage, but also strives to realize the multi-level coordination of the transmission network, distribution network and microgrid. Disorderly charging and discharging of large-scale electric vehicles (EVs) will have a great negative impact on the distribution network, but aggregating EVs and guiding them to charge and discharge in an orderly manner will play a positive role in delaying investment in the distribution network. Therefore, it is urgent to adopt an effective scheduling control strategy for electric vehicle charging and discharging. First, a variety of indexes were set to analyze the influence of EVs access on distribution network and the correlation between the indexes. Then, by defining the EVs penetration rate and the load simultaneous rate, the charging load planning of EVs was calculated. Based on the simultaneous load rate, the regional electricity load plan was calculated, and a configuration model of distribution capacity suitable for charging loads in different regions was constructed. Finally, an optimal dispatch model for electric vehicles considering the safety of distribution network was proposed and the distribution transformer capacity allocation model was used as the optimization target constraint. Compared with most optimized dispatch models used to maximize aggregator revenues and reduce peak-to-valley differences and load fluctuations in distribution networks, this model could effectively reduce unnecessary investment while meeting regional distribution transformer needs and maintaining distribution network security. Taking the improved IEEE 34-bus systems as an example, the simulation analysis was carried out and the investment demand of distribution network under the condition of disordered and orderly charge and discharge was compared. The results show that the proposed optimal scheduling method can effectively reduce the load fluctuation of distribution network, keep the voltage offset within the allowable voltage deviation range, and can effectively delay the investment of distribution network. [ABSTRACT FROM AUTHOR]