Browsing by Subject "Power system planning"
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Item Electric transmission system expansion planning for the system with uncertain intermittent renewable resources(2013-05) Park, Heejung; Baldick, RossThis dissertation proposes a new transmission planning method for electric power systems with large planned additions of uncertain intermittent renewable resources. The major contribution of this dissertation is applying stochastic programming that represents two uncertain parameters, wind and load, to transmission planning. We apply an ad hoc partition method to approximate the bivariate random variables of load and wind. A two-stage stochastic transmission planning problem is repeatedly solved by replacing continuous random variables with approximations that have a more refined partition at each iteration. A candidate solution is provided when improvement is not observed at an optimal value, even with more refined approximations. Numerical results show the efficiency of the method. However, if the number of samples is not sufficient to represent the original random variable's characteristics, the solution may be poor. Therefore, we employ a sampling method using Gaussian copula in order to generate as many random samples as necessary. The problem is replicated and solved using a fixed number of samples generated by Gaussian copula. In order to asses solution quality, a 95\%-confidence interval on the optimality gap is formed. A candidate stochastic solution for transmission investment is used to simulate the operation of a utility-scale storage system. A mixed integer program (MIP) is applied to this formulation. As a case study, the Electric Reliability Council of Texas (ERCOT) wind and load data is employed, along with a simplified model of the transmission system. Energy storage is also considered. The storage operation shifts wind power from off-peak hours to on-peak hours, and its wind power generation shows a close character to that of a base load generator.Item Studying present and future electric vehicle impacts on the city of Austin's power grid(2023-04-21) Ghose, Dipanjan; Santoso, Surya; Mohammadi, Javad, Ph. D.As Electric Vehicles (EVs) continue to grow in the market, they invite mixed reactions from different stakeholders. A major concern is whether the present electrical infrastructure can support the additional load generated by the electrification of the transportation sector. Thus, an intricate analysis of the load changes induced by EVs, both now and in the future, is needed to vouch for the grid’s reliability. For this study, a synthetic grid with a current installed capacity of 3813.6 MW, serving 307,236 customers, is selected. It is based on Austin Energy’s transmission network over the city of Austin, Texas. A DC-Optimal Power Flow approach is then used to test the grid’s supply and demand balance in different EV growth scenarios through 2030 and 2050. Predictions from the federal, state, and local administrative levels are used to model the future number of EVs. For the load shape, a simulated average EV charging curve is superimposed with projected hourly loads in Austin based on historical demand growth. The daily peak load is varied from 2116.69 MW with 28,964 EVs on an average day in 2023 to 4352.91 MW with 1.26 million EVs in 2050. Our analysis shows that the grid’s capacity can sustain till 2030, but falls short by 539 MW in 2050. The results from this study replicate Austin’s current EV growth and can form a basis for utilities like Austin Energy to plan their expansion in the coming years