Optimal capacity planning of combined renewable energy source-pumped storage and seawater desalination systems

As water scarcity is becoming a growing threat to human development, finding effective solutions has become an urgent need. To make better use of water resources, seawater desalination and storage systems using renewable energy sources(RES) are designed and implemented around the world. In this paper, an optimal capacity planning method for RES-pumped storage-seawater desalination(RES-PS-D) system is introduced. The configuration of the RES-PS-D system is clarified first, after which a cost-benefit analysis is performed using all cost and benefit components. A function for determining maximum economic benefits of the RES-PS-D system is then established, and the constraints are proposed based on various limitations. The mixed-integer linear programming algorithm is applied to solve the optimal function. A case study is introduced to validate the feasibility and effectiveness of the method. The conclusion shows that the strategy is suitable for solving the configuration optimization problem, and finally both merits and defects of the method are discussed.

Multi-period Two-stage Robust Optimization of Radial Distribution System with Cables Considering Time-of-use Price

In the existing multi-period robust optimization methods for the optimal power flow in radial distribution systems,the capability of distributed generators(DGs)to regulate the reactive power,the operation costs of the regulation equipment,and the current of the shunt capacitor of the cables are not considered.In this paper,a multi-period two-stage robust scheduling strategy that aims to minimize the total cost of the power supply is developed.This strategy considers the time-ofuse price,the capability of the DGs to regulate the active and reactive power,the action costs of the regulation equipment,and the current of the shunt capacitors of the cables in a radial distribution system.Furthermore,the numbers of variables and constraints in the first-stage model remain constant during the iteration to enhance the computation efficiency.To solve the second-stage model,only the model of each period needs to be solved.Then,their objective values are accumulated,revealing that the computation rate using the proposed method is much higher than that of existing methods.The effectiveness of the proposed method is validated by actual 4-bus,IEEE 33-bus,and PG 69-bus distribution systems.

Statistical scenarios forecasting method for wind power ramp events using modified neural networks

Wind power ramp events increasingly affect the integration of wind power and cause more and more problems to the safety of power grid operation in recent years.Several forecasting techniques for wind power ramp events have been reported.In this paper,the statistical scenarios forecasting method is proposed for wind power ramp event probabilistic forecasting based on the probability generating model.Multi-objective fitness functions are established considering cumulative density functions and higher order moment autocorrelation functions with respect to the consistency of distribution and timing characteristics,respectively.Parameters of probability generating model are calculated by the iterative optimization using the modified genetic algorithm with multi-objective fitness functions.A number of statistical scenarios captured bands are generated accordingly.Eventually,ramp event probability characteristics are detected from scenarios captured bands to evaluate the ramp event forecasting method.A wind plant of Bonneville Power Administration with actual wind power data is selected for calculation and statistical analysis.It is shown that statistical results with multi-objective functions are more accurate than the results with single objective functions.Moreover,the statistical scenarios forecasting method can accurately estimate the characteristics of wind power ramp events.The results verify that the proposed method can guide the generation method of statistical scenarios and forecasting models for ramp events.

Analytical Model of Day-ahead and Real-time Price Correlation in Strategic Wind Power Offering

In this paper,the model of strategic wind power offering in the day-ahead(DA)market is proposed considering the uncertainties of wind power production,and price forecasting of DA and real-time(RT)market.The wind power deviation in the RT market is settled with the two-price mechanism based on the deviation direction and the relation between the locational marginal prices(LMPs)of DA and RT.Instead of using the point forecasting for the DA and RT LMPs,the uncertainties of LMP forecasting are modeled.In addition,the correlation between the forecasting errors of DA and RT LMP are directly modeled instead of generating the correlated scenarios.Finally,the opitmal offered quantity of wind power in the DA market is derived using the probability theory based on the probabilistic wind power forecasting.The case study using the price data of actual DA and RT from Midcontinent Independent System Operator(MISO)validates the effectiveness of the proposed model.It shows that the correlation of the forecasting errors of DA and RT LMP has a significant impact on the wind power quantity offered by DA and revenue results.

Primal dual interior point dynamic programming for coordinated charging of electric vehicles

Coordinated charging of electric vehicles(EVs)is critical to provide safe and cost effective operation of distribution systems where household single phase charging of EV could contribute to imbalance of the distribution system.To date,reported researches on optimization methods for coordinated charging aiming at minimizing power losses have the disadvantages of low calculation efficiency when applied to large systems or have not taken the voltage constraints into account.The phase component and polar coordinates power flow equations of an unbalanced distribution system are derived.Primal dual interior point dynamic programming is introduced for coordinated charging of EVs to minimize distribution system losses where charging demand,voltage and current constraints have been taken into account.The proposed optimization is evaluated using an actual 423-bus case as the test system.Results are promisingwith the proposed method having good convergence under time-efficient calculations while providing optimization of power losses,lower load variance,and improvement of voltage profile versus uncoordinated scenarios.

A comparative study on grid resource utilization rate between China Southern Power Grid and National Grid Plc of UK

Grid resource utilization rate plays a key role in power grid operation.Although the proportion of population and the area in China Southern Power Grid(CSG)and National Grid Plc of UK(NG)are nearly,grid resources utilization of CSG is lower than that of NG.In this paper,the utilization of human resources and equipment resources of CSG and NG have been reviewed.It is discovered that there is a large gap between these two companies.The results show that the utilization in CSG is about 20%to 50%of that in NG.And it is estimated that if the equipment resources utilization of CSG increases to the level of NG in 2012,the coal consumption and thus CO2 emissions would be reduced by 17 million tons and 21 million tons,respectively.As China is a developing country and Britain is a mature developed country,it is to some extent reasonable that grid resources utilization of CSG is lower than that of NG.The benefits of improving equipment resources utilization are illustrated in detail,which provided the reference for developing the grid resource utilization in China.