Abstract
The significant development and increasing deployment of renewable generation in the modern power system introduces the challenge for dealing with uncertainty. In this paper, data-adaptive robust optimization is applied to the transmission network planning. By taking historical data correlation into account, the proposed model can achieve a lower expansion investment without sacrificing the robustness. Demand response is embedded in this model to relieve the overflow incurred by renewable generation fluctuation and $N-1$ contingency. The model is decomposed into a master problem and several slave problems by column and constraint generation algorithm and then solved iteratively. The numerical simulation tested on Garver 6-bus system and the IEEE 118-bus system demonstrates the effect of demand response in reducing or postponing network construction. The proposed data-adaptive robust optimization is proved to be cost-effective and computationally efficient.
Highlights
Transmission network expansion planning (TNEP) aims to serve the forecasted demand sufficiently and reliably with minimal investment on the electrical installation during a given planning horizon.Demand response (DR) has been rapidly developing in recent years as a kind of flexible resource coping with various problems in the power system
This paper proposes a DAR-TNEP model considering wind power uncertainty and N − 1 contingency, the contribution can be listed as follows: 1. The uncertainty is tackled with data-adaptive robust optimization (DARO) to obtain a less conservative solution and lower planning cost compared to traditional robust optimization
The experiment is performed on a personal computer with Intel CoreTMi5-6200U CPU (2.3GHz) and 8GB of memory, using Matlab 2014b and Gurobi 7.0.2 as the solver. It is indicated by the results of 0, 2%, 8% and 10% DR ratios that DR acts as an alternative for transmission line expansion to achieve a lower total cost
Summary
Transmission network expansion planning (TNEP) aims to serve the forecasted demand sufficiently and reliably with minimal investment on the electrical installation during a given planning horizon. Unlike the traditional robust optimization which describes the realization of the uncertain parameters with an interval or polyhedral uncertainty set, DARO shrinks the realization region by utilizing the historical data correlation, and reduces the conservativeness [31]. Reference [30] co-optimizes renewable generation and load reserve with a chance constrained optimal power flow model and solves it with a distributionally robust approach. The effect of DARO is investigated in reducing the operation cost for a power system with uncertain resources, the number of literature incorporating DARO with the TNEP problem is quite limited. The uncertainty is tackled with DARO to obtain a less conservative solution and lower planning cost compared to traditional robust optimization.
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