Static Transmission Expansion Planning Research Articles

Robust Static Transmission Expansion Planning Considering Contingency and Wind Power Generation

This paper proposes a framework based on the Benders decomposition to obtain a scenario-based robust static transmission expansion planning by considering N-1 security criterion, transmission losses and uncertainties in wind power generation. The model is solved by a bi-level approach that seeks to minimize investment cost as well as penalty costs of wind spill and load curtailment. The wind uncertainty is modeled by grouped historical wind series through k-means clustering technique maintaining the wind correlation between different geographic regions. Case studies are performed in the well-known power systems: IEEE-RTS 24-bus test system and an equivalent Brazilian Southern 46-bus system. In addition, a detailed tutorial case is also presented with a modified version of Garver 6-bus test system.

Optimal static and dynamic transmission network expansion planning

In this paper, a novel approach is proposed to solve the problem of static and dynamic Transmission expansion planning (TEP) in electrical power systems. The proposed method is the execution of an Adaptive salp swarm optimization algorithm (ASSO). The ASSO is a meta-heuristic algorithm, which depends on the swarming behavior and population of salps. In the proposed approach, ASSO is the charge of acquiring the best candidate solution for the expansion planning and locate the optimal solution for both the static and the dynamic transmission network expansion in perspective of the minimum error objective function. The objective is formulated to minimize the cost of investment in transmission systems by satisfying the constraints such as active power balance, expansion parameter, and load shedding restrictions. The proposed ASSO technique ensures the system with less complexity, reduced computational time and hence the efficiency of the system is raised. Finally, the proposed model is executed in MATLAB/Simulink working stage and the execution is compared with the existing techniques.

Evaluation of GA Performance in TNEP Considering Voltage Level, Network Losses and Number of Bundle Lines

Many models were presented to solve the static transmission network expansion planning (STNEP) problem by previous research. However, in these models, lines’ voltage level and losses were not studied in STNEP. Therefore, in present paper, static transmission expansion planning is investigated by considering lines’ voltage, losses and bundles using decimal codification genetic algorithm (DCGA). The DCGA is better than mathematical methodologies to solve large-scale, nonlinear and mixed-integer optimization problems, like the TNEP. The proposed method is tested on the real transmission network of Azarbaijan regional electric company, Iran. The results show that operation costs decreases considerably and the transmission system delivered more safe and reliable electric power to customers if the network losses, voltage levels and the number of bundle lines are considered in transmission expansion planning.

Differential evolution algorithm for static and multistage transmission expansion planning

A novel differential evolution algorithm (DEA) is applied directly to the DC power flow-based model in order to efficiently solve the problems of static and multistage transmission expansion planning (TEP). The purpose of TEP is to minimise the transmission investment cost associated with the technical operation and economical constraints. Mathematically, long-term TEP using the DC model is a mixed integer nonlinear programming problem that is difficult to solve for large-scale real-world transmission networks. In addition, the static TEP problem is considered both with and without the resizing of power generation in this research. The efficiency of the proposed method is initially demonstrated via the analysis of low, medium and high complexity transmission network test cases. The analysis is performed within the mathematical programming environment of MATLAB using both DEA and conventional genetic algorithm and a detailed comparative study is presented.

A Metaheuristic to Solve the Transmission Expansion Planning

In this letter, a genetic algorithm (GA) is applied to solve the static and multistage transmission expansion planning (TEP) problem. The characteristics of the proposed GA to solve the TEP problem are presented. Results using some known systems show that the proposed GA solves a smaller number of linear programming problems in order to find the optimal solutions and obtains a better solution for the multistage TEP problem.