Integrated Full-Infinite Programming Methods For Energy Systems Management

Based on the rapid industrialization and urbanization, economy is growing fast, which leads to increasing of energy consumption. A lot of serious environmental problems have also been created. Energy systems is a complex and huge system, with various uncertainties to identify. In that case, how to express the uncertainties and their interactions, how to reflect these interactions to a optimization model and how to determine suitable methods are all key issues which planners have to face to. To solve these problems, this paper would focus on the dynamic and complexity for multi-objective, multi-period, multi-scenario, and multi-element based on the analysis of energy systems. A series of integer full-interval programming (FIP) methods would be developed:(1) an interval full-infinite programming (IFIP) method. IFIP integrates FIPinto an interval mathematical programming (IMP) framework, which is capable of addressing multiple uncertainties existing in related costs, impact factors and system objectives expressed as determinates, crisp interval values and functional intervals. Then, IFIP is applied to an energy planning system. According to the results, the amount of energy allocation, electricity supply and pollution emission of4power plants would be generated.(2)an interval full-infinite mixed-integer programming (IFMIP) method. IFMIP is based on an integration of existing IFIP and mixed-integer linear programming (MILP) techniques. IFMIP is utilized to a real case study of energy systems planning in Beijing. It can facilitate capacity-expansion planning for energy-production facilities, and coordinate the conflict interactions among economic cost, system efficiency, pollutant mitigation and energy-supply security.(3) a full-infinite fuzzy stochastic programming (FFSP) method. FFSP combines fuzzy mathematical programming method andstochastic mathematical programming to a FIP framework, and FFSP can deal with uncertainties presented in terms of fuzzy sets, random variables, and functional interval values. FFSP is applied to a case study of Beijing for managing electric power systems (EPS), and reducing the GHG emission by introducing the European Union greenhouse gas emission trading scheme.(4) a risk-explicit mixed-integer full-infinite programming (RMFP) method. Considering high risks in carbon emission trading, RMFP is developed for risk reflection and policy analysis by introducing a risk explicit IMP and two-stage stochastic programming to a FIP framework under various uncertainties. RMFP is applied to plan carbon emission trading of EPS in Beijing. The results are useful for voiding the system-failure risk, and help gaining insight into the tradeoffs among electricity supply risk, system cost, and CO2mitigation strategy.(5) an interval-parameter chanced-constrained full-infinite mixed-integer programming (ICFMP) method. ICFMPintegrates IMP, FIP, MILP, and chanced-constrain programming in to an optimization framework. ICFMP can support the assessment of the reliability of satisfying systems constraints. ICFMP applied to energy systems planning in Beijing. The results are useful for making decisions of energy production and allocation under different probabilities as well as gaining insight into the tradeoffs between the system cost and the constraint-violation risk.(6) an interval-parameter full-infinite joint-probabilistic mixed-integer programming (IFJMP) method. By incorporation of "joint probability", IFJMPcan examine the reliability of satisfyingsystem constraints under uncertainty. IFJMP is then applied for planning EPS of Beijing. With the aid of IFJMP, tradeoffs among system costs, electricity-supply security, and air-pollution control can be obtained under joint probabilities.(7) a multistage stochastic full-infinite integer programming (MSFIP) method. For reflect the dynamics through generation of a set of representative scenarios, MSFIP is introduced to reduce the system risks, associated with multiple uncertainties. A case study for EPS is provided for demonstrating the applicability of the MSFIP, which is able to help for lowering the risk of system failure due to potential violation when determining optimal electricity remediation strategies.(8) a full-infinite interval-stochastic mixed-integer programming (FIMP) method. FIMP is applied to of EPS in Beijing for managing CO2emissions with trading scheme, and achieve optimized carbon emission permits of different power plants under uncertainty. The solutions can be used for CO2reduction and assessing the associated economic implications in purchasing emission permits or bearing economic penalties.In this paper, a series of integrated FIP modes are developed, and they have capacity to assist decision makers better deal with the system complicated uncertainties and possible risks. Results cover all aspects of energy systems, includingenergy supply and demand, electricity production and supply, pollutant emission control, carbon trading's implementation, minimize of system cost, etc. These solutions can support the adjustment of the existing plans and policies, and facilitation of dynamic analysis for decisions of capacity expansion and development plans. It is worth to mention that the solutions not only can provide scenario analysis for government departments and decision-makers, but also could offer forward-looking policies of energy systems, and gradually resolve problems which decision maker may encounter in the future. It would realize the unification of economic gains, social improvements and environmental benefits.