• Energy Research

Due to the existence of complexities of heterogeneities, hierarchy, discreteness, and interactions in municipal solid waste management (MSWM) systems such as Beijing, China, a series of socio-economic and eco-environmental problems may emerge or worsen and result in irredeemable damages in the following decades. Meanwhile, existing studies, especially ones focusing on MSWM in Beijing, could hardly reflect these complexities in system simulations and provide reliable decision support for management practices. Thus, a framework of distributed mixed-integer fuzzy hierarchical programming (DMIFHP) is developed in this study for MSWM under these complexities. Beijing is selected as a representative case. The Beijing MSWM system is comprehensively analyzed in many aspects such as socio-economic conditions, natural conditions, spatial heterogeneities, treatment facilities, and system complexities, building a solid foundation for system simulation and optimization. Correspondingly, the MSWM system in Beijing is discretized as 235 grids to reflect spatial heterogeneity. A DMIFHP model which is a nonlinear programming problem is constructed to parameterize the Beijing MSWM system. To enable scientific solving of it, a solution algorithm is proposed based on coupling of fuzzy programming and mixed-integer linear programming. Innovations and advantages of the DMIFHP framework are discussed. The optimal MSWM schemes and mechanism revelations will be discussed in another companion paper due to length limitation.

Abstract In this research, a fuzzy interval-parameter approach considering constraint-violation and energy-substitution effects (FIP-CVESE) was developed for planning municipal energy systems, which was an integration of interval linear programming (ILP) and fuzzy linear programming (FLP), as well as methods of constraint-violation and energy-substitution analyses. The proposed method was then applied to a real-world case for supporting the planning of the energy system in Beijing. The developed FIP-CVESE method could not only tackle uncertainties expressed as both interval numbers and fuzzy sets, but also allow several given levels of tolerable violations of system objective and constraints to expand the model's decision space, facilitating the improvement in solution robustness. The obtained results could provide a series of desired decision alternatives at preferred satisfaction degrees, including schemes related to energy resources allocation, technology adoption, facility capacity expansion, transportation device identification, and environmental pollution mitigation. Also the substitution solutions among multiple energy resources can be generated by combining energy substitution effects with the violation levels of resources demands constrains through the introduction of violation variables. The acquired solutions could also help decision makers gain insights of trade-offs between the economy and environment, as well as support decision-making for the planning of the energy system in Beijing.

As presented in the first companion paper, distributed mixed-integer fuzzy hierarchical programming (DMIFHP) was developed for municipal solid waste management (MSWM) under complexities of heterogeneities, hierarchy, discreteness, and interactions. Beijing was selected as a representative case. This paper focuses on presenting the obtained schemes and the revealed mechanisms of the Beijing MSWM system. The optimal MSWM schemes for Beijing under various solid waste treatment policies and their differences are deliberated. The impacts of facility expansion, hierarchy, and spatial heterogeneities and potential extensions of DMIFHP are also discussed. A few of findings are revealed from the results and a series of comparisons and analyses. For instance, DMIFHP is capable of robustly reflecting these complexities in MSWM systems, especially for Beijing. The optimal MSWM schemes are of fragmented patterns due to the dominant role of the proximity principle in allocating solid waste treatment resources, and they are closely related to regulated ratios of landfilling, incineration, and composting. Communities without significant differences among distances to different types of treatment facilities are more sensitive to these ratios than others. The complexities of hierarchy and heterogeneities pose significant impacts on MSWM practices. Spatial dislocation of MSW generation rates and facility capacities caused by unreasonable planning in the past may result in insufficient utilization of treatment capacities under substantial influences of transportation costs. The problems of unreasonable MSWM planning, e.g., severe imbalance among different technologies and complete vacancy of ten facilities, should be gained deliberation of the public and the municipal or local governments in Beijing. These findings are helpful for gaining insights into MSWM systems under these complexities, mitigating key challenges in the planning of these systems, improving the related management practices, and eliminating potential socio-economic and eco-environmental issues resulting from unreasonable management.

Abstract Old industrial bases in Northeastern China have been experiencing a series of problems such as energy scarcity, economic slack and environmental pollution due to lack of scientific planning of energy systems. Effective energy system management is desirable for avoiding occurrence of these problems in the following decades under the national policy of revitalizing these industrial bases. This task is challenged by system complexity, data unavailability, and inaccurate demand forecasting which can hardly be resolved in existing studies. For improving energy system management in Qiqihar City, a representative industrial base in Northeastern China, under these challenges, a large-scale and fine-resolution Bayesian interval robust energy system optimization (BIRESO) approach is developed in this study. In detail, the structure, characteristics, problems and complexities of the energy system in this city are identified and analyzed. Based on these efforts, a series of optimized energy system management schemes are generated through construction of a BIRESO model and development of a solution algorithm. The obtained solutions can be ensured to remain robust when input data changes through controlling the degree of solution conservatism in accordance with probabilistic bounds on constraint violation. Furthermore, Bayesian estimation method was employed for effectively achieving dynamic forecast of energy demands. The tradeoff among energy security, economic development and environmental conservation under multiple uncertainties is revealed, and the optimal balance among them is identified. A scenario analysis approach is used to quantify the effects of economic growth on energy system schemes based on several developed scenarios. Results show that the BIRESO approach is capable of providing scientific support for energy supply planning, renewable energy development, technological advancement, facility expansion, energy allocation, pollution control, economic impacts assessment, and other energy related activities in Qiqihar City.

Regional energy-environment systems management become more and more focused on greenhouse gas emission control through improving energy efficiency and efficiently managing energy activities. Inexact linear programming models are developed for supporting the management. Due to the weather/climatic variations in the future, electricity demands and renewable power generations (in the right/left hand sides of constraints) have random characteristics. Moreover, an overall satisfactory level needs to be quantified based on multiple chance constraints. Therefore, this study improved upon traditional chance-constrained programming and interval linear programming, and developed an interval joint-probabilistic two-side chance-constrained programming (IJTCP) approach. A sufficient but non-equivalent linearization form of the model was proposed so that the inexact model could be solved through the two-step solution algorithm. The IJTCP was then applied to an integrated energy-environment systems management under dual uncertainties. The application demonstrated that the IJTCP can effectively address the uncertainties presented as not only interval numbers and two-side multi-randomness but also the reliability of satisfying the entire system constraints. The application implicated that the IJTCP approach can be applied to other energy-environment management problems under dual uncertainties.

An interval type-2 fuzzy fractional programming (IT2FFP) method is developed for planning the renewable energy in electric power system for supporting sustainable development under uncertainty. IT2FFP can tackle output/input ratio problems where complex uncertainties are expressed as type-2 fuzzy intervals (T2FI) with uncertain membership functions. The IT2FFP method is then applied to planning Beijing electric power system, where issues of renewable energy utilization, electricity supply security, and pollutant/greenhouse gas (GHG) emissions mitigation are incorporated within the modeling formulation. The obtained results suggest that the coal-fired power would continue to decrease and the share of renewable energy in gross electricity supply would maintain an increasing trend. Results also reveal that imported electricity plays a significant role in the city’s energy supply. A number of decision alternatives are also analyzed based on the interval solutions as well as the projected applicable conditions, which represent multiple options with sustainable and economic considerations. The optimal alternative that can give rise to the desirable sustainable option under the maximization of the share of renewable power generation has been suggested. The findings can help decision makers identify desired alternatives for managing such a mixed energy system in association with sustainable development. Compared with the conventional optimization methods that optimize single criterion, it is proved that IT2FFP is advantageous in balancing conflicting objectives and reflecting complicated relationships among multiple system factors as well as in tackling various subjective judgments of decision makers with different interests and preferences.

Systematically evaluating the emission intensity and total emission of industries is indispensable for understanding energy and environmental sector performance in general and to support scientific climate change policy-making. In this study, an environmentally extended input-output (EEIO) model with a detailed disaggregation of energy sectors is developed to investigate the life-cycle environmental impacts of different industries. A special case study of the Province of Saskatchewan, Canada, is conducted to illustrate the potential benefits of its use in the environmental policy-making field. The I–O table is transformed and disaggregated based on the energy use patterns and the underlying economic structure. Key GHG emissions, including CO2, CH4 and N2O, are considered and the CO2 equivalent intensities of different economic sectors are calculated. An in-depth analysis of key industries is conducted to further investigate the interactions between different industries. It is founded that the Province of Saskatchewan is a trade exposed and emission intense economy. The emission intensity of agriculture is higher than the mean level, and is difficult to reduce due to the large farm machines used in agricultural production. Fossil-fuel electric power generation, as an intermediate input, has a strong effect on other industries and is a key factor for emission reduction.

In this study, an inexact coupled coal and power management (ICCPM) model was developed for planning coupled coal and power management systems through integrating chance-constrained programming (CCP), interval linear programming (ILP) and mixed integer linear programming (MILP) techniques. The ICCPM model can effectively handle uncertainties presented in terms of probability density functions and intervals. It can also facilitate dynamic analysis of capacity expansions, facility installation and coal inventory planning within a multi-period and multi-option context. Complexities in coupled coal and power management systems can be systematically reflected in this model, thus applicability of the modeling process would be highly enhanced. The developed ICCPM model was applied to a case of long-term coupled coal and power management systems planning in north China. Interval solutions associated with different risk levels of constraint violations have been obtained, which can be used for generating decision alternatives and helping identify desired policies. The generated results can also provide desired solutions for coal and power generation, capacity initiation and expansion, and coal blending with a minimized system cost, a maximized system reliability and a maximized coal transportation security. Tradeoffs between system costs and constraint-violation risks can also be tackled.