Energy Saving-economic Scheduling Of Multi-energy Flow Based On Exergy Evaluation

In recent years,multi energy supply technology represented by cooling,heating and electric power is rapidly developed in the reginal integrated energy system.The optimal scheduling of multi energy has become the focus of the recent research area.Exergy efficiency is employed in this thesis as the evaluation index.The multi energy flow system model is established with cooling,heating and electric power based on the exergy flow.The most energy-saving and economic scheduling strategy of the multi energy system is optimized.The main research contents and contributions are as following:(1)A multi energy flow system model including cooling,heating and power of industrial park level is established.The input energy of the system includes natural gas thermoelectricity and distributed renewable energy generation of wind power generation system.The demands are cooling,heating and electric power.Advanced adiabatic compressed air energy system(AA-CAES)is introduced as an energy storage device to absorb redundant wind power and supply heating energy in the multi energy supply system.(2)In this thesis,the ‘exergy analysis’ method is introduced to evaluate the energy-saving index of the system.When the system has the highest exergy efficiency,it is the most energy-saving.The multi energy flow system supplies energy to the determined cooling,heating and power load,the highest efficiency is equal to the least exergy input.Taking the park level multi energy system as an example,using the typical daily cooling,heating and electrical power load data in summer,winter and spring/autumn seasons,the minimum exergy input is used as the objective function to carry out the multi energy day-ahead scheduling.The most energy-efficient and economical dispatching result is got.The system exergy input and energy cost per hour are calculated.Wind power is the renewable energy and does not need to be purchased.Therefore,the most economical choice is that the wind power should be given priority to supply electric power.Electrical energy is a high-grade energy with high efficiency.Therefore,when wind energy is the first choice to meet the system energy demand the energy conservation is consistent with the system economy.(3)Simulated annealing algorithm is used to solve the most energy-saving and economic day-ahead dispatch problem.The traditional simulated annealing method has some limitations and can only solve nonlinear unconstrained problems.The problem to be solved in this thesis is a nonlinear constrained problem which can’t be solved by the traditional simulated annealing method.So it needs to be improved.The penalty function is added to the algorithm to transform the nonlinear constrained problem into the minimum problem of augmented objective function.The particle swarm optimization(PSO)algorithm is improved by introducing second-order oscillation and probabilistic suboptimal solution to improve the search accuracy and avoid falling into local optimum.The results of the two algorithms are compared and analyzed,and the improved simulated annealing algorithm is used to solve the optimal scheduling strategy.(4)The multi energy system without AA-CAES is also modeled and energy-saving economic scheduling is carried out.The efficiency is compared with that of the multi energy flow system with AA-CAES.The results show that when AA-CAES is installed,the system exergy efficiency is higher.It is about 38%-58%.The exergy efficiency without AA-CAES is about 27% to 48%.