Abstract
Transmission Expansion Planning (TEP) involves determining if and how transmission lines should be added to the power grid so that the operational and investment costs are minimized. TEP is a major issue in smart grid development, where demand response resources affect short- and long-term power system decisions, and these in turn, affect TEP. First, this paper discusses the effects of demand response programs on reducing the final costs of a system in TEP. Then, the TEP problem is solved using a Teaching Learning Based Optimization (TLBO) algorithm taking into consideration power generation costs, power loss, and line construction costs. Simulation results show the optimal effect of demand response programs on postponing the additional cost of investments for supplying peak load.
Highlights
Installing new devices on an existing power system while ensuring stability and reliability of the power system are the main goals of Transmission Expansion Planning (TEP)
The investment problem is solved by an evolutionary method, while the generation problem is solved by a known optimization method
This can be done by reducing the incentive price or increasing the limitations of customer participation operation of the network (Before Demand response programs (DRPs) and TEP), DRP is applied in a way that customer participation in DRP
Summary
Installing new devices on an existing power system while ensuring stability and reliability of the power system are the main goals of Transmission Expansion Planning (TEP). The main drawback of this method is that the optimal solution may get stuck in the local optimal point, and problems are associated with the determination of the initial values of an unknown load flow Another optimization method for solving the transmission line expansion is mathematical decomposition, which is one of the first methods formulated by Pereira et al [15]. The results of the method presented in [29] show that a demand response program can significantly reduce generation operating and investment costs Another method for TEP under generation uncertainty was proposed by Konstantelos and Strbac [30], where the potential for flexible network technologies, such as phase-shifting transformers, and non-network solutions, such as energy storage and demand-side management, were assessed. An incentive-based demand response program was implemented for peak load reduction and, in turn, reduction of TEP costs This method can be considered similar to integrating Distributed.
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