Enhanced multiobjective algorithm for transmission expansion planning considering N − 1 security criterion

Summary Over the past few years, the penetration of renewable distributed generation (DG) into power systems has increased markedly. Given the intermittency and uncertainty of renewables, demand response (DR) resources play an important role in balancing power supply‐demand and shaving peak demand. In order to evaluate the system performance under contingencies, a risk factor is explicitly defined in this paper. This risk factor realistically considers the cost and the feasibility of corrective control (CC) actions. This risk factor is incorporated into our multi‐objective transmission expansion planning (TEP) framework, which handles conflicting objectives of cost, risk, and reliability. The proposed model is numerically verified on the modified IEEE 118‐bus and IEEE 24‐bus RTS systems. Comparative studies with simplified versions of TEP models have also been undertaken. To sum up, the proposed approach should be considered as a risk‐based, decision support tool, providing system operators or network planners with opportunities to make tradeoffs between cost, risk and reliability.

min_{{boldu}_{0}} f ((),, {boldχ}_{0}, {boldu}_{0})

s.t. g_{k} ((),, {boldχ}_{k}, {boldu}_{k}) = 0, k = 0, 1, true\dots K

g_{k}^{tran} ((),, {boldχ}_{k}^{italictran}, {boldu}_{0}) = 0, k \in Ω_{C}^{C}

h_{k} ((),, {boldχ}_{k}, {boldu}_{k}) \leq h_{k}^{\max}, k = 0, 1, true\dots K

h_{k}^{tran} ((),, {boldχ}_{k}^{italictran}, {boldu}_{0}) \leq β_{k} h_{k}^{\max}, k \in Ω_{C}^{C}

(||, u_{k} - u_{0}) \leq ε_{k}^{\max}, k = 0, 1, true\dots K

where subscript k = 0 corresponds to the pre‐contingency state, while k > 0corresponds to the post‐contingency state. The total number of contingencies is denoted by K .…”

Section: Corrective Control (Cc) Risk Indexmentioning

confidence: 99%

Power network planning considering trade‐off between cost, risk, and reliability

Qiu

Meng

Zhao

et al. 2017

“…These multiple generation scenarios are governed by the availability of generators, weather conditions, fluctuation of fuel prices, etc. Static long‐term TNEP is solved by an enhanced NSGA‐II algorithm in 1 study and by strength Pareto evolutionary algorithm 2 in previous study . Both of these methods produce several Pareto‐optimal solutions, which are then considered by a decision maker on the basis of their merits.…”

Section: Introductionmentioning

confidence: 99%

Transmission network expansion planning using a modified artificial bee colony algorithm

Das

Verma

Bijwe

2017

Summary Transmission network expansion planning (TNEP) problem is an essential part of power system expansion planning, and it is an extremely complex nonlinear, nonconvex, mixed‐integer optimization problem. Solution to such a computationally intensive problem is a challenge for any optimization algorithm. Consideration of security constraints makes the problem even more formidable. Although various conventional and metaheuristic methods have been used in the past to solve such problem, scope for better optimization techniques always remain. The artificial bee colony (ABC) algorithm is one of the newest swarm intelligence‐based optimization algorithms, which has delivered promising results in solving numerical optimization problems. However, the algorithm is quite less efficient in solving real‐life constrained engineering problems. In this paper, a modified ABC (MABC) algorithm is formulated by incorporating the idea of global attraction, universal gravitation, and by introducing modified ways of searching in various bees' phases of the ABC algorithm. The MABC is able to get better results in a very efficient manner, when used for solving various benchmark functions. The efficiency and effectiveness of the MABC algorithm in solving constrained engineering problems is demonstrated by solving TNEP problems for different systems. The proposed method is tested on IEEE 24 bus system, South Brazilian 46 bus system, Colombian 93 bus system for direct current TNEP model, and Garver 6 bus system for alternating current TNEP model. Results confirm that MABC can be an attractive alternative to the existing optimization algorithms for solving very complex nonlinear engineering optimization problems in a real‐world situation.

“…This is done by considering the contradiction of the objective functions and their many numbers, different stakeholders, and also the difference in the importance degree of each of the objectives based on the planner 0 s standpoint. [14][15][16][17] In addition, the focus on applying flexible AC transmission system (FACTS) devices has recently increased, due to various reasons, including the existence of various generation and consumption models in power markets, and constraint of the expansion options. Fixed series compensations (FSCs) are one of the series FACTS devices which are able to control the amount of the active power flow by reducing the natural reactance of transmission lines.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective transmission expansion planning with allocation of fixed series compensation under uncertainties

Moradi

Alinejad‐Beromi

Kiani

2017

Summary Integration of wind resources into the power systems has increased the technical and financial concerns in transmission expansion planning. In this paper, a stochastic structure is demonstrated for transmission expansion planning with allocation of fixed series compensation under wind and load uncertainties. Fixed series compensations have the ability to increase the transfer capacity of transmission lines. However, their significance benefit for transmission expansion planning is their higher effectiveness in power dispatching, which results in lower investment costs, compared with planning without fixed series compensations. The proposed planning model simultaneously optimizes targets such as the investment cost, the congestion cost, and the expected energy not supplied in each stage. The presented planning model is a complicated nonlinear optimizing problem. For introducing group of nondominated optimal solutions, multi‐objective gray wolf optimizer algorithm based on probabilistic optimal power flow is used. Then, fuzzy approach is used to lead to the best response. For demonstrating the feasibility and susceptibilities of the proposed algorithm, the planning methodology has been illustrated on the IEEE 24‐bus and Colombian test systems. The results acquired from the simulations monitor capability of the proposed structure in order to satisfy various planning indices.