Co-optimization Of Planning And Operation Of Gas-Electricity-heat Integrated System With Energy Hub

Energy hub is a center which realizes the conversion of multiple energy resources by coordination of its components.The gas-electricity-heat integrated energy system(GEH-IES)which includes the production of electricity,gas and heat is the future architecture of Energy Internet.The energy hub with storage facilities controls the input and output of multiple energies to improve the energy efficiency and satisfy the energy demand.Different energies are integrated and complementary,which improves the operational efficiency of the whole system.For the planning and operation of IES,first of all,the electricity,gas and heat demands should be satisfied;then,the optimal operation is considered.Besides,the influence of load forecasting on system operation should also be investigated due to the uncertainty of load consumption.The works in this thesis are summarized as follows.For the uncertain electricity load demand,we propose the optimal-ARIMA-based load forecasting method which selects the parameters using the AIC criterion.The load forecasting provides information for the planning and operation of systems.Moreover,the chaos theory and phase space reconstruction are introduced;thus,the load forecasting method based on chaotic time series is proposed,which further improves the reliability of forecasting.Further,we adopt a clustering method based on load shape similarity which provides useful information for system optimal operation.The structure and operating mechanism of GEH-IES are investigated.The optimization model is established for the optimal planning and operation of IES with energy hub.The objective is to minimize the constructing cost,operating cost and load shedding cost.The operation strategies of generators,CHP units and gas turbines are studied,and the influence of the integration of various energies is analyzed.The established optimization model for the planning and operation of GEH-IES is nonlinear.Therefore,the variable substitution technique and the piecewise-linearization method are used to approximate nonlinear terms;thus,the optimization problem is transformed into a mixed-integer linear programming(MILP)problem,which is solved by the CPLEX software.Simulation results indicate the effectiveness of the established model and the proposed method.