Optimal Allocation of Wind Turbines by Considering Transmission Security Constraints and Power System Stability

Abstract

Abstract:A novel optimization methodology consisting of finding the near optimal location of wind turbines (WTs) on a planned transmission network in a secure and cost-effective way is presented on this paper. While minimizing the investment costs of WTs, the algorithm allocates the turbines so that a desired wind power energy-penetration level is reached. The optimization considers both transmission security and power system stability constraints. The results of the optimization provide regulators with a support instrument to give proper signals to WT investors, in order to achieve secure and cost effective wind power network integration. The proposal is especially aimed at countries in the initial stage of wind power development, where the WT network integration process can still be influenced by policy-makers. The proposed methodology is validated with a real power system. Obtained results are compared with those generated from a business-as-usual (BAU) scenario, in which the WT network allocation is made according to existing WT projects. The proposed WT network allocation scheme not only reduces the total investment costs associated with a determined wind power energy target, but also improves power system stability.

Algorithm for optimal wind power plant capacity allocation in areas with limited transmission capacity

“…This work proposes a simulation framework for assessing the possibility of WPP integration into the existing transmission network in an optimal manner that takes into account the wind potential at each site scheduled for WPP construction [7,8]. This work proposes a simulation framework for assessing the possibility of WPP integration into the existing transmission network in an optimal manner that takes into account the wind potential at each site scheduled for WPP construction [7,8].…”

“…production = 1 MW) of i-th WPP in j-th moment dP S k net power injection of conventional power plants and loads connected to the k-th bus for characteristic system state S N size of normalized WPP production sample BR set of branches in considered transmission network C WPP max i maximum construction capacity for i-th WPP nb n number of system buses (without slack buses) The expression in Equation (1) assumes linear costs of WPP construction [8]. production = 1 MW) of i-th WPP in j-th moment dP S k net power injection of conventional power plants and loads connected to the k-th bus for characteristic system state S N size of normalized WPP production sample BR set of branches in considered transmission network C WPP max i maximum construction capacity for i-th WPP nb n number of system buses (without slack buses) The expression in Equation (1) assumes linear costs of WPP construction [8].…”

An Optimization Framework for Investment Evaluation of Complex Renewable Energy Systems

“…The constraints in (15) ensure that the nominal installed capacity of an ESS at a given site i ∈ V 1 ∪ V 2 (s n i ) is a portion (given by σ i ) of the maximum reference capacity S max . The amount of energy that can be stored at a given ESS located at i ∈ V 1 ∪ V 2 , at a period t ∈ T (s it ), cannot exceed its corresponding capacity s n i ; this is enforced by the constraints in (16).…”

How Spatial Relationships Influence Economic Preferences for Wind Power—A Review

“…As argued in Rahmann and Palma-Behnke [8], locations with favorable wind regimes might be located in weak or challenged transmission areas, whereas less favorable wind regime locations might be near a stronger transmission area that is also close to load (demand) centers. In that case, the location with favorable wind conditions might exhibit higher generation costs relative to sites with less favorable wind conditions.…”