Research On Optimal Scheduling Of Combined Cooling,Heating And Power Microgrids

With the depletion of globalized fossil energy and the increasingly prominent environmental problems,the combined cooling heating and power microgrid,which can not only consume renewable energy but also improve energy efficiency,has received more and more attention in academia.However,the output of renewable energy in the microgrid has a strong randomness,the scheduling of cooling and heating power energy in the microgrid is coupled,and the characteristics of both the power generation and user sides' devices connected to the microgrid are becoming more and more complicated.It has caused great difficulties for the optimal scheduling of the microgrid.Firstly,this paper has studied the economic scheduling problem of combined heating and power microgrid based on interval programming theory.In this part,we use the simplified distributed power model to build an optimal scheduling model.In related literature,the computational efficiency of the microgrid optimal scheduling model based on interval programming is often limited by the number of equation constraints.In this paper,the duality theorem is used to convert a large number of linear"pessimistic models" into a non-linear model.At the cost of increasing the complexity of the model,the number of pessimistic models has been greatly reduced,which is conducive to improving computing efficiency.In view of some shortcomings of traditional particle swarm optimization algorithm,this paper proposes an adaptive chaotic particle swarm optimization algorithm based on dynamic penalty function for the solution of pessimistic model.Secondly,considering the more complex model of distributed generation,this paper proposes a mixed-integer linear programming model for multi-objective optimal scheduling of combined cooling,heating and power microgrid with the demand response project.In this model,the objective function includes the operating cost of the microgrid,pollution gas treatment costs,and peak load reduction.This paper introduces an improved ?-constraint method for calculating the Pareto optimal solution set of multi-objective optimization problems,and the simulation result is compared with the traditional multi-objective intelligent algorithm.The proposed model and method lay a foundation for further research on stochastic optimal scheduling for microgrid based on mixed integer linear programming.Finally,this paper studies the problem of multi-objective two-step stochastic optimization scheduling of combined heating and power microgrid based on scenario analysis method.The first step of the optimization model is the day-ahead multi-objective stochastic optimization.The entropy weight method is used to determine the harmful weight of pollutant gas emissions.The Latin hypercube sampling method and the synchronized generation reduction method are used to generate stochastic scenarios with probability weights.The second step of the optimization model is 5-minute rolling optimization,and the day-ahead scheduling plan is revised in real time according to the actual situation.This paper converts the stochastic optimization model containing random variables and mathematical expectation expressions into a deterministic mixed integer linear programming form,which is easy to solve with CPLEX software.The simulation results prove that the model and algorithm proposed in this paper have the characteristics of high efficiency and accuracy,which means that they can fully meet the needs of real-time optimization scheduling.