Research On The Optimization Of Hybrid Power Systems With Renewable Energy

Owing to the ever increasing energy consumption and serious environmental pollutions,the interest in renewable energy has already drawn more attention.Applying renewable energy into a power system can achieve the goal of reducing the system cost and greenhouse gas emissions.This paper proposes an optimal planning method for various types of power systems,which are transmission system,hybrid ship power system and microgrid.The main contents of this research are presented as follows.Firstly,a Hybrid Multi-Objective Particle Swarm Optimization(HMOPSO)approach is proposed in the paper to minimize the power system cost and improve the system voltage profiles by searching sitting and sizing of storage units under consideration of uncertainties in wind power production.Furthermore,the probability cost analysis is first put forward in this paper.The proposed HMOPSO combines Multi-Objective Particle Swarm Optimization(MOPSO)algorithm with elitist non-dominated sorting genetic algorithm(NSGA-II)and probabilistic load flow technique.It also incorporates a 5-point estimation method(5PEM)for discretizing wind power distribution.Secondly,with the increasing penetration of wind power,not only the uncertainties but also the correlation among the wind farms should be considered in power system analysis.In this paper,Clayton-Copula method is developed to model the multiple correlated wind distribution and a new point estimation method for joint distribution is proposed to discretize the multi-correlated wind distribution.Furthermore,combining the proposed modeling and discretizing method with HMOPSO,a comprehensive algorithm is developed to minimize the power system cost and reduce the emission by searching the best placements and sizes of energy storage system considering wind power uncertainties in multi-correlated wind farms.In addition,the variations of load are also taken into account.Thirdly,with the rapid development of smart grid,this paper sets up a microgrid based on IEEE 34-bus distribution system which consists of wind power generation system,photovoltaic generation system,diesel generation system,and energy storage system associated with various types of load.Furthermore,the Particle Swarm Optimization(PSO)algorithm is proposed in the paper to minimize the power loss and improve the system voltage profiles by optimally manage the different sorts of distributed generations under consideration of the worst condition of renewable energy production.Fourthly,due to the serious environmental pollution and low efficiency problem of traditional ships,the application of solar energy and energy storage system(ESS)into a ship power system has drawn more attention.In this study,a series of experiments are performed to investigate the characteristic of a photovoltaic(PV)system with a moving ship.Based on the experimental results,an interval uncertainty model of on-board PV generation is established,which considers the date,local time,time zone,longitude,latitude and the effect of the swinging of the ship.The proposed method considers five conditions along the navigation route to model the total ship load.An interval optimization method is developed to determine the optimal size of the ESS in this hybrid ship power system to reduce the fuel cost,capital cost of the ESS,and emissions of greenhouse gases.Finally,this paper proposes an optimization algorithm for optimally sizing of ESS in a hybrid wind/PV/diesel/ ship power system.Unlike on land,the output power of wind turbines on the shipboard is changing not only with the wind speed but also the ship speed.Therefore,this paper develops a method to correct the output of the wind power generation on the shipboard which take wind speed,wind direction,ship course and speed into account.With the help of the proposed MOPSO,both the cost and emissions of the ship are reduced dramatically.