Reliability-constrained dynamic transmission expansion planning considering wind power generation

Abstract

This paper proposes a two-stage framework to solve the long-term transmission network expansion planning (TNEP) problem ensuring user-defined reliability levels and wind curtailment over the planning horizon. In the first stage, the static TNEP (S-TNEP) problem is solved to define where a set of new lines must be installed at the end of planning horizon. In the second stage, a multistage procedure is performed to solve the dynamic TNEP (D-TNEP) in order to determine the moment that each transmission line should be built. Both S-TNEP and D-TNEP are decomposed into investment decision and performance assessment through a bi-level scheme based on Benders decomposition; thus, the reliability is considered and evaluated over the planning process. The reliability and performance indexes are obtained through the non-chronological Monte Carlo simulation considering the random behavior of transmission lines failures, load fluctuations and uncertainties over wind availability. The loss of wind probability performance index is introduced to measure the probability of wind curtailment in each connection point of the system, and it is used to prevent wind farms from being underused. The proposed methodology is tested in modified versions of the 24-bus IEEE reliability test system and the IEEE 118-bus test system. Keywords Transmission expansion planning • Wind power generation • Reliability assessment • Monte Carlo simulation • Benders decomposition • Short-term uncertainties Sets and subscripts B Set of system buses E Set of existing lines C Set of candidate lines Ω E i Set of existing lines connected to bus i Ω C i Set of candidate lines connected to bus i i System bus i j Line between buses i and j k Existing or candidate transmission lines s Operative state B A. N. de Paula

A data‐driven inertia adequacy‐based approach for sustainable expansion planning in distributed generations‐penetrated power grids

“…The optimization problem (36) can be easily solved by the WASP-IV software [23,50,51]. However, to tackle the inertia adequacy problem in the GEP formulation, the total amount of load to be met in year t' in constraint (37) should be re-formulated as P G WASP = P forcast ,t ′ − P DGs (38) where P G, WASP is the input load data to WASP software. Indeed, (38) reveals that to extend the WASP-IV software to tackle the inertia adequacy problem in the formulation, the dynamical-based inertia adequacy index of ( 35) is re-interpreted as a negative load.…”

“…A dynamic hybrid model for GEP formulation, considering high voltage alternating current (HVAC) lines, high voltage direct current (HVDC) lines, and RESs is discussed in [36]. Reliability-constrained-based TEP problem considering wind power generation is addressed in [37]. A scenario-based robust static TEP problem formulation, considering transmission line losses, the N−1 criterion, and uncertainties over wind power generation, is discussed in [38].…”

Stochastic expansion planning of transmission system and energy hubs in the presence of correlated uncertain variables

“…Stochastic formulation of the TEP/GTEP problem taking into account deployment of RESs/ESSs has been discussed in refs. [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][44][45][46][47]. In addition, refs.…”

“…The power production of a WT is presented in Eq. [32]. If the wind speed takes amounts lower than the cut-in speed or higher than the cut-out speed, the WT generation will be zero.…”

Wind Power Prediction Based on Difference Method

“…According to statistics, a record high, 71.67 million kilowatts of new grid-connected capacity was installed in 2020, and wind power generation was 466.5 billion KWH, an increase of about 15%. However, due to the highly random fluctuations and intermittent nature of the wind and the influence of the tail flow between units in the wind farms, the large-capacity wind turbines connected to the grid pose serious challenges to the security of the power system, power quality and the balance of power a record high, demand and supply [3]. Korprasertsak et al have conceptualized statistical interpretation of multiple forecasting models for short-term prediction of wind power generation under uncertainty to study the fluctuation characteristics of wind power, improve the accuracy of real-time wind power prediction, and to overcome the adverse impact of wind power access on the grid [4].…”