Transmission Expansion Planning Studies Research Articles

Long-Term Multiyear Transmission Expansion Planning in Turkish Power System

To sustain the clean energy transition without interruption and to ensure the reliable operation of the transmission system, it is required to have enough additional transmission capacity in the future horizons. The transmission expansion planning (TEP) problem is a core issue in deciding additional transmission capacity in the planning activities. TEP aims to find the best expansion plan while satisfying technical and economic constraints. In this study, a new binary version of the original FBI algorithm called the BFBI (binary forensic-based investigation) algorithm is developed to solve the binary TEP problem. The effectiveness and performance of the developed BFBI are assessed by implementing it in two different test systems: the standard Garver 6-bus test system and the modified 400 kV Turkish grid. Seasonal scenarios are created for 5- and 10-year planning periods to cover all possible generation and load conditions and to assess the impact of the increased share of RES on the grid in the TEP studies conducted for the modified 400 kV Turkish grid created as a bulk realistic grid. The TEP problem is solved by including investment, reliability, and operational costs in two different objective functions for cases while considering the N-1 contingency criterion. The efficacy and robustness of the BFBI algorithm are justified by comparing it with well-known algorithms such as GA and PSO.

A Test System for Transmission Expansion Planning Studies

This paper introduces a 17-bus 500 kV test system intended for transmission expansion planning (TEP) studies. The overhead lines used in the system are based on an actual 500 kV transmission line geometry. Although several test systems have been developed for various forms of power system analysis, few are specifically tailored for TEP studies at the transmission voltage level, as opposed to the distribution voltage level. Current test systems for TEP studies are limited to single loading conditions only for normal operating conditions, and the majority of these systems are intertwined with issues related to the energy market or devised specifically for integrating new generations and loads into the existing power systems. However, ensuring a test system satisfies both voltage drop and line loading criteria during both normal and all single contingency operations is crucial in TEP studies, and addressing these issues under contingency conditions poses notable challenges. Moreover, practical TEP scenarios involve varied loadings, including peak load and dominant loading (60% of peak load) scenarios, while the existing test systems are configured solely for single loading conditions. To address these technical gaps, this paper introduces the 17-bus test system operating at a transmission voltage level of 500 kV, meeting technical requirements under normal and all single contingency operations for both peak load and dominant load scenarios. Detailed specifications of the proposed test system and load flow analysis at both normal and contingency conditions for different loading conditions are presented. This test system serves as an invaluable resource for TEP studies.

Developing a Robust Expansion Planning Approach for Transmission Networks and Privately-Owned Renewable Sources

Power system restructuring has changed transmission expansion planning (TEP) and caused many complications due to conflicting and contradictory objectives. The transmission capacity expansion would significantly affect the revenue of investor-owned renewable energy sources (RESs). Thus, the investment decisions on merchant RESs must be considered in the TEP studies conducted by the transmission system operator (TSO). In this regard, this paper aims to propose a multi-objective co-planning of investment in transmission networks and merchant RESs with three objective functions: minimizing the investment cost of newly deployed transmission lines, minimizing transmission congestion cost, and minimizing load curtailment in N-1 conditions. Moreover, the TSO guarantees a desirable rate of return for private investors to finance renewable energy projects. Further, a robust optimization (RO) technique is employed to cope with the uncertainties associated with demand and renewable energy production. Also, a posteriori multi-objective optimization algorithm, i.e., the non-dominated sorting genetic algorithm (NSGAII), is applied to solve the advanced optimization problem, followed by the fuzzy min-max method to acquire the final optimal solution. Finally, the IEEE RTS 24-bus test system is utilized to demonstrate the effectiveness and applicability of the suggested approach.

A new adequacy evaluation method for transmission expansion planning problems

Transmission Expansion Planning (TEP) problem refers to proposing the optimal number and location of new transmission lines in order to satisfy operation conditions with less investment costs. Adequacy evaluation of trial solutions is essential to solve TEP problem. This evaluation procedure may require a considerable computational e ort; therefore, improvement of Adequacy Evaluation Methods (AEMs) is a key to achieve more ecient TEP solution algorithms. It is a common practice to employ a Linear Programming (LP) called DC operation to perform this evaluation, especially in cases that generation rescheduling is considered in TEP studies. In this paper, an AEM is proposed as an alternative with less computational e ort to perform adequacy evaluation. In fact, the proposed AEM is applicable in TEP problems considering generation rescheduling. The study is based on DC TEP model where electrical aspect of the problem is modeled based on DC power flow equations. To examine the performance of the proposed AEM, this evaluation method is employed in a meta-heuristic TEP solution algorithm. Such a combinatorial algorithm is then applied to di erent standard test systems as well as practical cases. The simulation results show a promising improvement caused by employment of the proposed AEM.

Probabilistic multi‐objective HVDC/AC transmission expansion planning considering distant wind/solar farms

This study presents a new binary differential evolution algorithm for probabilistic AC/DC transmission expansion planning (TEP). The probabilistic model would consider the uncertainty of distant wind/solar energy resources as well as load demand. In many power systems, the necessity of considering the impacts of HVDC links on the TEP studies is inevitable. Therefore, in this study, these links are considered a substitute candidate for the conventional HVAC transmission lines. The stochastic modelling and integration of HVDC links for TEP problem have introduced new challenges to transmission system planners. To overcome these problems, an efficient fuzzy set-based optimisation technique is used to solve the multi-objective TEP problem. Finally, the proposed method is applied to the test system consisting of 24 buses.

Incorporating Large-Scale Distant Wind Farms in Probabilistic Transmission Expansion Planning—Part I: Theory and Algorithm

With increment in the penetration of wind energy in power systems, the necessity of considering its impacts on transmission expansion planning (TEP) studies, especially for large scale wind farms, is inevitable. A new multi-objective (MO) optimization transmission expansion planning algorithm considering wind farm generation is presented in this two-paper set. Part I is mainly devoted to derivation of the theory and algorithm. The objective functions used in the TEP studies take into account investment cost, risk cost and congestion cost. The combination of Monte Carlo simulation (MCS) and Point Estimation Method (PEM) is implemented to investigate the effects of network uncertainties. Due to its comparative assessment potential and good handling of the non-convex problems and non-commensurable objective functions, the Non-Dominated Sorting Genetic Algorithm II (NSGA II) is widely used for evaluating the MO optimization problem. Eventually, for selecting the final optimal solution, a Fuzzy decision making approach is applied based on decision maker preferences.