• Energy Research

Abstract In this study, an integrated fuzzy possibilistic-joint probabilistic mixed-integer programming (FPJPMIP) model is developed and applied to the expansion planning of power generation under uncertainty. As an extension of existing fuzzy possibilistic programming and joint probabilistic programming, the FPJPMIP addresses system uncertainties in the model's left- and right-hand sides (with the expression of possibilistic and probabilistic distributions). Its applicability has been demonstrated by the application to a hypothetic power generation problem. The developed method is applied to a case of power generation expansion planning, where desirable solutions are obtained. Willingness to pay higher costs will promise system stability. A desire to reduce the costs will get into the risk of potential system failure.

Abstract Increasing urbanization in the world brings tremendous social, economic and environmental challenges. It is essential to fully analyze urban GHG emissions metabolism systems to reveal economic emissions reduction pathways and support sustainable development. In this study, a factorial-based ecologically-extended input-output (FEEIO) model is developed to facilitate urban GHG emissions metabolism analysis. A special case study of the Province in Saskatchewan, Canada, is conducted to illustrate the potential benefits of its use in urban metabolism system health diagnosis. A factorial analysis is introduced to further investigate the effects of the main factors and their interactions. It is found that an urban GHG emissions metabolism system differs from other metabolism systems in regards to its special structure. A high efficiency represents limited emissions pathways in an urban GHG emissions metabolism system, which further provides good opportunities to realize GHG emissions mitigation. In the Province of Saskatchewan, the urban GHG emissions system has high redundancy and low efficiency across twenty scenarios. The GHG emissions from other sources are much simpler than emissions from coal, which further indicates that the emissions from other sources are easier to control through technology improvements or industrial regulations for specific sectors.

Selecting an appropriate wind farm location must be specific to a particular administrative region, which involves restrictions balance and trade-offs. Multi-criteria decision making (MCDM) and GIS are widely used in wind energy planning, but have failed to achieve the selection of an optimal location and make it difficult to establish a set of independent factors. Fuzzy measurement is an effective method to evaluate intermediate synthesis and calculates the factor weight through fuzzy integrals. In this paper, optimal wind farm location is analyzed through coupling Grid GIS technique with λ fuzzy measure. Dalian City is selected as the study area for proving the feasibility of the proposed method. Typography, meteorological, transmission facilities, biological passage, and infrastructure are taken into the index system. All the indexes are specialized into victor grid cells which are taken as the base wind farm location alternative unit. The results indicate that the Grid GIS based λ fuzzy measure and Choquet fuzzy integral method could effectively deal with the special optimization problem and reflect optimal wind farm locations.

Abstract In this study, a fractile-based interval mixed-integer programming (FIMP) method is advanced for sustainable municipal-scale energy system planning and management. FIMP can handle uncertainties presented in terms of fuzzy boundary intervals that represent interval coefficients with independently fuzzy lower and upper bounds with possibility distributions. A FIMP-based municipal energy model (FIMP-MEM) is then formulated for managing various energy activities in the City of Shenzhen, China. Solutions for energy supply, electricity generation, oil-product production, air-pollutant mitigation, carbon dioxide control, capacity expansion, and electricity import/export are obtained. Results can be used to help the city's managers to identify desired system designs and to determine which of these designs can most efficiently accomplish optimizing the system objective under diverse p-necessity fractiles. The generated decision alternatives are beneficial for the city's energy system planning and management through (a) generating desired energy resources allocation, (b) identifying electricity generation and capacity-expansion scheme, (c) providing air pollution control plan, (d) analyzing the tradeoff among system cost, environmental impact, and system-failure risk.

Abstract In this study, a feasibility based inexact fuzzy programming approach (FBIFP) was developed and applied to the planning of a regional electric power generation system (REPGS). As an extension of existing interval linear programming and fuzzy linear programming, FBIFP could tackle uncertainties expressed as intervals, fuzzy sets as well as their combination in both of the objective function and constraints. Through introducing the index of feasibility degree, FBIFP could be used to facilitate the efficient reflection of constraint violation, and thus allow decision makers to have a complete view of the relationships between uncertain inputs and the related solutions. A hypothetic regional electric power generation system was provided for demonstrating applicability of the developed model. Useful solutions for the planning of REPGS were generated. Interval solutions under different feasibility degrees were obtained. They could be used for helping decision makers identify desired alternatives under various reliability risks of the system. The solutions could also help identify optimal patterns for energy resources allocation, electricity generation and facility capacity expansion with a minimized system cost. Tradeoffs between system costs and system security could be successfully addressed through the analysis of the feasibility degrees of constraints, i.e., a higher feasibility degree would correspond to a higher system cost, while a lower system cost would run into a higher risk of potential instability of the study system.

Energy-related activities contribute a major portion of anthropogenic greenhouse gas (GHG) emissions into the atmosphere. In this study, a dual-interval multi-stage stochastic programming model for the planning of integrated energy-environment systems (DMSP-IEES) model is developed for integrated energy-environment systems management, in which issues of GHG-emission mitigation can be reflected throughout the process of energy systems planning. By integrating methodologies of interval linear programming (when numbers are described as interval values without distribution information), dual-interval programming (when lower and upper bounds of interval values are not available as deterministic values but as discrete intervals), and multi-stage stochastic programming, the DMSP-IEES model is capable of dealing with uncertainties expressed as discrete intervals, dual intervals, and probability distributions within a multi-stage context. Decision alternatives can also be generated through analysis of the single- and dual-interval solutions according to projected applicable conditions. A case study is provided for demonstrating the applicability of the developed methodology. The results indicate that the developed model can tackle the dual uncertainties and the dynamic complexities in the energy-environment management systems through a multi-layer scenario tree. In addition, it can reflect the interactions among multiple system components and the associated trade-offs.

Energy is crucial in supporting people’s daily lives and the continual quest for human development. Due to the associated complexities and uncertainties, decision makers and planners are facing increased pressure to respond more effectively to a number of energy-related issues and conflicts, as well as GHG emission mitigation within the multiple scales of energy management systems (EMSs). This quandary requires a focused effort to resolve a wide range of issues related to EMSs, as well as the associated economic and environmental implications. Effective systems analysis approaches under uncertainty to successfully address interactions, complexities, uncertainties, and changing conditions associated with EMSs is desired, which require a systematic investigation of the current studies on energy systems. Systems analysis and optimization modeling for low-carbon energy systems planning with the consideration of GHG emission reduction under uncertainty is thus comprehensively reviewed in this paper. A number of related methodologies and applications related to: (a) optimization modeling of GHG emission mitigation; (b) optimization modeling of energy systems planning under uncertainty; and (c) model-based decision support tools are examined. Perspectives of effective management schemes are investigated, demonstrating many demanding areas for enhanced research efforts, which include issues of data availability and reliability, concerns in uncertainty, necessity of post-modeling analysis, and usefulness of development of simulation techniques.

Abstract Reliable access to electricity is still a serious problem for remote villages, however, abundant solar radiation and biomass have the potentials to provide energy supply in a sustainable way. This paper focuses on the optimal design and techno-economic analysis of the stand-alone hybrid energy system for a typical rural village in the northwest of China. Based on the estimation of local renewable energy potential and the investigation of heat and power consumption patterns, a mixed-integer linear programming optimization model is presented with the aim of minimum total annualized cost. The techno-economic analysis of different system configurations is conducted and compared. Particularly, the levelized cost of energy production and levelized cost of energy consumption is proposed to evaluate the economic-cost of energy from both supply and demand-side respectively. Furthermore, sensitivity analysis and scenario analysis is conducted to demonstrate the influences of the variations and uncertain environmental policy on total system cost and performance. The results demonstrate that the PV/biomass-CHP/diesel generator/gas boiler/battery/thermal energy storage hybrid energy system is the most economical option with a total annualized cost of 652 × 103 $. The levelized cost of energy consumption (0.355 $/kWh) is greater than that of energy production (0.275 $/kWh), implying a great potential for local electrification and more flexible heat/power output equipment.

Abstract A novel strategy was proposed for the improvement of vapor chamber heat pipe (HP) performance. The mastoid process array, wettability control of heat pipe and control of liquid charged into heat pipe are comprehensively used to manage the phase distribution. Thus, the two dominant heat transfer mechanisms of nucleation and convection can be switched. The objective of this strategy is to maintain nucleation mechanism but suppress convection mechanism. Holding the nucleation mechanism, our newly developed heat pipe behaves excellent thermal sensitivity with respect to heating loads. Our idea was thoroughly verified by experiments, in which three HP samples were fabricated and tested: #1 equal wettability sample, #2 moderate wettability difference sample and #3 opposite wettability sample. The wettability difference was generated with nano-structured surface. The #1 sample shows convection mechanism with heat transfer coefficients (HTCs) not changed versus heat fluxes, due to evaporator porous wick occupied by larger vapor content and liquid filmwise condensation on condenser surface. The super-hydrophilic evaporator and super-hydrophobic condenser (#3 sample) shows nucleation mechanism with HTCs significantly increased with increases in heat fluxes, due to large driving force to flood evaporator porous wick by water and expose condenser by vapor. Surprisingly, opposite wettability match HP had overall thermal resistances of only 1/3 to those of equal wettability HP. The comprehensive use of mastoid process array and controls of wettability and water amount charged into HP lowers heater temperatures by 30–40 °C at high heat fluxes such as ~100 W/cm2 on a large heater area of 1.4 cm2, paving a new road for heat pipe performance improvement.