| Due to incremental population growth, high-speed economic development, and accelerated urbanization process, cities’energy demand and consumption have been undergoing high speedy increases. Such tendency has exacerbated the contradiction between supply and demand, and brought about energy system potential insecurity problems of insufficient power supply and heavy dependence on fossil fuels and/or import electricity, as well as environmental problems of air pollution and excessive greenhouse gases’emission. Municipal energy system could be thought of as an interrelated and complex network connected by some various processes involving energy exploitation, processing, conversion, transmission and consumption. The uncertainties may be derived from these processes, as well as their interactions. These inherent complexities and uncertainties of municipal energy system are have significant effects on energy resources flows, electricity consumption mix, and even relevant decision alternatives to be made by electricity sector decision makers, which intensify insecurity issues of electricity supply. Accordingly, how to abstract related mathematical problems in real-world problem problems, how to formulate mathematical models associated with features and inherent relationships of municipal energy system, how to apply mathematical programming approaches to quantify multiple formats of uncertainties in municipal energy system, how to reflect the potential interactions among uncertain parameters, how to reveal the link between parameter uncertainty through to decision uncertainty and their interactions and implications, are crucial for municipal energy system planning.For above-mentioned challenges, based on municipal energy system analysis, this thesis will incorporate concept of necessity measure into modeling framework which improve upon traditional fuzzy mathematical programming method. By combining fuzzy possibilistic programming, fuzzy robust programming, interval mathematical programming and two-stage stochastic programming, a series of robust fuzzy optimization (RFO) methods would be proposed for municipal energy system management, and RFO-based municipal energy system models would be formulated: (1) a fractile-based robust stochastic programming (FRSP) method. FRSP improves upon existing fuzzy robust programming by introducing necessity measure, and can address fuzzy sets and probability distributions in objective function and constraints. FRSP is applied to long-term municipal electric power systems planning, and FRSP-based municipal electric power system model is formulated, where two cases are examined based on different GHG and air-pollutant management policies. Municipal energy structure adjustment schemes under different cases would be generated, and the effects on energy consumption and system cost would be analyzed. (2) a fractile-based fuzzy interval mixed-integer programming (FIMP) method. FIMP incorporates fuzzy possibilistic programming and interval-parameter programming, such that dual uncertainties presented in terms of fuzzy boundary intervals in the objective function can be tackled. A FIMP-based municipal energy model (FIMP-MEM) is then formulated for a real case of managing energy system in the City of Shenzhen. FIMP-MEM can obtain Shenzhen’s optimized schemes for energy supply, electricity generation, capacity expansion, and air-pollutant mitigation, and analyze the tradeoff among system cost, environmental impact, and system-failure risk. (3) a fuzzy-interval possibilistic programming (FIPP) method. In FIPP, parameter uncertainties expressed as crisp intervals and fuzzy-boundary intervals can be effectively tackled by introducing expected value operator and necessity degree level. Through integrating clean development mechanism (CDM) into municipal energy optimization model formulation, a FIPP-based clean development mechanism (FIPP-CDM) model is then formulated. Solutions concerning CO2 emission abatement, CDM project activity, and energy mix would be generated. Tradeoffs among CO2-emission reduction, energy supply-demand constraint violation risk, power supply security and economic objective would be made in-depth analyses. Several findings reveal the impact mechanism of clean development on municipal electric power system carbon emission abatement and impacts of uncertain parameters on CDM framework and municipal electric power system, which are beneficial for decision makers to support the enactment of future policies concerning carbon emission abatement and city’s low-carbon development under CDM. (4) a robust possibilistic mixed-integer programming (RPMP) method. In RPMP, the concept of robustness level and necessity degree level is incorporated within a possibilistic mix-integer programming framework to handle ambiguous uncertainties in the objective function and constraints. It is superior to existing fuzzy programming method by accounting for recourse actions of deviation of imprecise objective function from expected value, as well as penalty of non-satisfied imprecise constraint RPMP can also minimize the penalty of risk, avoid imposing high risks to decision makers and help seek for solutions with optimality and feasibility robustnesses. A RPMP-based electric power system (RPMP-EPS) model has been formulated for power supply risk analysis of the City of Shenzhen. By analyzing electricity consumption mix, as well as electricity balance and self-sufficiency condition, cost-effective and sustainable power supply schemes would be obtained. Potential risk of municipal power supply and the impacts of energy policy on electric power system would be analyzed. Interactions among control level of decision makers’s risk attitude, system cost, and supply security are analyzed. Results show that uncertainties have significant effects on municipal energy resources flow, power consumption mix and system cost.In this paper, a series of robust fuzzy optimization methods are proposed, which deal with the complexities and uncertainties of municipal energy system and CDM framework, and reveal the impacts of multiple uncertain parameters on model’s outputs as well as potential interactions among uncertain parameter. Besides, a series of uncertain municipal energy system models involving various processes of energy supply, energy processing, electricity generation, capacity expansion, energy transmission, end-use energy consumption, greenhouse gases and air-pollutant emission, CDM project implementation, awarded certified emission reduction credits are formulated. Findings will be helpful for adjustment of city’s energy consumption structure towards a cleaner pattern, transition of city’s electricity supply pattern towards self-sufficient, secure and cost-effective ways, formulation of future policies concerning renewable energy development and electricity supply security, analysis of interactions among energy system, economy, environment, and market, investigation of energy decisions that need to be made periodically over time, as well as assistance of municipal energy management. |
PDF Full Text Request