Probabilistic Approaches To Network Expansion Planning Of Power Systems With Wind Farms

In order to deal with the world-wide energy crisis,wind farms have been developed rapidly in recent years to generate electric power from renewable wind power.However,wind power is variable and intermittent and it therefore introduces an extra factor of uncertainties for power system operation and planning. The network with large proportion of wind power will have more power flow fluctuations.Therefore,the deterministic transmission network expansion methods, which only account for one operation scenario,are unsuitable for the planning with stochastic power output from wind farms.This paper proposes to tackle the uncertainties of wind farms in transmission network expansion planning with probabilistic methods to obtain robust transmission network expansion planning schemes.First,a fast deterministic transmission network expansion planning model is proposed based on the traditional model.The way to reflect the overload level of a candidate planning scheme in the new model is changed by replacing the loss of load item with total overload percent.The linear optimization using in determining the loss of load item avoided and hence the computational time is shorter.Although different formulations are used to penalize the overload schemes,the proposed and traditional models can obtain the same optimal solution with minimized cost if a planning scheme without load curtailment and overload can be achieved finally.In order to take uncertainties of both load variation and wind farm power output into consideration,a chance constrained transmission network expansion planning formulation is proposed by expanding the deterministic formulation into probabilistic area.Different from the conventional Monte Carlo simulation approach to obtain the final probability distribution,this paper has also proposed a solution approach which combines the Monte Carlo simulation with the analytical method.The combined method reduces the computational cost greatly and makes it possible to introduce the wind farm model and probabilistic power flow in the proposed chance constrained transmission network expansion planning.In order to accelerate the optimization process,a two step genetic algorithm has been used and found to be efficient.The study examples have shown the necessity in including the uncertainties of load and wind farm in the transmission network expansion planning and has illustrated that the proposed chance constrained method for transmission network expansion planning can consider these uncertainties and provide much more comprehensive information,including the effects of these uncertainties in planning schemes and the relationship between the investment cost and the risk of overload,which are essential in order to achieve a robust and cost-effective planning scheme.The probabilistic load flow evaluation is a powerful approach to investigate the steady-state power system operation characteristics under various possible uncertainties which has been widely used in transmission network expansion planning.Monte Carlo simulation combined with simple random sampling is one of the most popular mathematical methods adopted in probabilistic problems and also has been widely used in PLF and many other power system analyses.However,the high accuracy of solutions can only be achieved by a large number of repeated calculations.This paper proposes the use of an efficient sampling method,Latin hypercube sampling combined with Cholesky decomposition method,into Monte Carlo simulation for solving the PLF problems.The proposed method can achieve a better sampling efficiency than simple random sampling and Latin hypercube sampling combined with random permutation,and makes it possible for Monte Carlo simulation to get an accurate simulation result with a much smaller simulation size.The proposed method is found to be robust and flexible and has the potential to be applied in many power system probabilistic problems.