GA Based Multi-stage Transmission Network Expansion Planning

The power systems are becoming more and more complex due to the inclusion of new components and increasing load demand. Consequently, it is imperative to incorporate additional generation units and transmission links into the system. Transmission Network Expansion Planning (TNEP) seeks to include generation units and transmission lines into the system at optimal locations and minimal costs. Mathematical techniques are extensively employed to address the problem. Nonetheless, mathematical methods necessitate extensive computation durations. Consequently, novel solution strategies are under investigation. The TNEP problem is characterized by an innovative and effective metaheuristic optimization techniques. This study presents a novel Opposition Based Learning and Fitness Distance Balance based Coati Optimization Algorithm (FDBCOA-OBL) designed to address Static and Dynamic TNEP problems. An extensive experimental investigation was undertaken to evaluate the efficacy of the suggested method in addressing the benchmark test suites and the TNEP problem. The FDBCOA-OBL algorithm, utilizing the Elite OBL approach, surpassed all other comparative versions in addressing the benchmark test problems. The Wilcoxon analysis indicates that it lost 6 problems, tied in 110, and won 166 problems. The proposed approach resolved the TNEP problem in 6, 25, and 93-bus test systems. The Static TNEP solution was applied to the 6 and 25 bus test systems, while the Dynamic Multistage TNEP method was utilized in the 93-bus test system. The acquired investment expenses were compared to the research already documented in the literature. The findings indicate that the suggested method demonstrates robust performance.

The authors are proposing a software-tool for transmission network expansion planning (TNEP). The developed software-tool is able to be used in case of complex, large scale power systems. It uses a.c. complete power flow mathematical model. Power flow computing is performed using conventional methods. Optimal power flow and network expansion are performed using artificial intelligence methods. Within this field, two methods have been tackled: particle swarm optimization (PSO) and genetic algorithms (GA).

Guest Editorial: Situational awareness of integrated energy systems

The paper is focusing on transmission network expansion planning (TNEP) problem solved using artificial intelligence techniques. It is divided into two parts. The 1st part is dedicated to the particle swarm optimization (PSO) and genetic algorithm (GA) concepts and mechanisms. The mathematical models and the associated software tool are also presented. Practical considerations are discussed. The 2nd part is focusing on case studies. 13 buses test power system, developed by the authors and IEEE 24 RTS have been used. The research work is going to be used in case of the Romanian power system (over 1000 buses).

The authors are focusing on transmission network expansion planning (TNEP) based on particle swarm optimization (PSO) method. The development algorithm has been implemented within a software tool. Two case studies have been used to present the results: IEEE 24 RTS (well-known from the literature) and 25 buses test power system (developed within the Power Systems Department). The developed methodology is able to be applied in case of a complex power system.

This paper presents a metaheuristic approach to solve the transmission network expansion planning (TNEP) problem considering non-conventional solution candidates. The TNEP consists on finding the set of new elements required in a power system to meet a given future demand at a minimum cost. The TNEP traditionally considers as candidate solutions the addition of new lines and transformers. The main contribution of this work is the inclusion of non-conventional solution candidates. Such non-conventional solution candidates are namely: repowering of existing circuits and reactive shunt compensation. Also, an AC modeling of the network that allows obtaining more realistic results than the traditional DC model has been considered. The TNEP is represented by means of a non linear mixed integer programming problem which is solved through a hybrid genetic algorithm (HGA). Several tests were performed on two benchmark power systems to show the applicability and effectiveness of the proposed approach.

Flexible Alternating Current Transmission Systems (FACTS) got in the recent years a well known term for higher controllability in power systems by means of power electronic devices. FACTS devices can effectively control the load flow distribution, improve the usage of existing system installations by increasing transmission capability, compensate reactive power, improve power quality, and improve stabilities of the power network. However, the location and settings of these devices in the system plays a significant role to achieve such benefits. This work presents the application of Real Coded Genetic Algorithm (RGA) and Particle Swarm Optimisation (PSO) for finding out the optimal locations, and the optimal parameter settings of multi type FACTS devices to achieve maximum system loadability (MSL) in the power system. The FACTS devices used are Thyristor Controlled Series Capacitor (TCSC) and Unified Power Flow Controller (UPFC). The reactance model of TCSC and the decoupled model of UPFC are considered in this work. The thermal limits of the line and voltage limits of the buses are taken as constraints during the optimization. Simulated Binary Crossover (SBX) and Non-uniform polynomial mutation are employed to improve the performance of the Genetic Algorithm used. Simulations are performed on IEEE 6 bus and 30 bus power systems. The obtained results are encouraging and show the effectiveness of RGA over PSO algorithm

A smart load management system based on the grasshopper optimization algorithm using the under-frequency load shedding approach

This paper presents a hybrid algorithm which integrates Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) for solving thermal unit commitment. The UC problem consists of two sub-problems: Unit Scheduled problem which is solved by PSO for minimization of transition cost and Economic Dispatch that can be solved by GA by the means of minimizing the production cost. The proposed algorithm is demonstrated for a system including ten thermal units. Running PSO and GA simultaneously justifies the production cost reduction in 24 hour period. Choosing varying PSO acceleration coefficients and inertia weight make the system convergence faster and skip the local optimums.

The optimal power flow (OPF) problem was introduced by Carpentier in 1962 as a network constrained economic dispatch problem. Since then, it has been intensively studied and widely used in power system operation and planning. In the past few decades, many stochastic optimization methods such as Genetic Algorithm (GA), Evolutionary Programming (EP), and Particle Swarm Optimization (PSO) have been applied to solve the OPF problem. In particular, PSO is a newly proposed population based stochastic optimization algorithm. The main idea behind it is based on the food-searching behavior of birds and fish. Compared with other stochastic optimization methods, PSO has comparable or even superior search performance for some hard optimization problems in real power systems. Nowadays, some modifications such as breeding and selection operators are considered to make the PSO superior and robust. In this paper, we propose the Modified PSO (MPSO), in which the mutation operator of GA is incorporated into the conventional PSO to improve the search performance. To verify the optimal solution searching ability, the proposed approach has been evaluated on an IEEE 30-bus test system. The results showed that performance of the proposed approach is better than that of the standard PSO.

This paper proposed the Lagrangian optimization model for Optimal Power Flow (OPF) problem. It is designed by relaxing the constraints from the Quadratic Programming (QP) problem. The objective of this model is to minimize the total cost of active power generation. The solution of QP is obtained by different optimization techniques like Particle Swarm Optimization (PSO) method, Genetic Algorithm (GA), Differential Evolution (DE) algorithm. In this paper, the optimum value of QP is obtained by the proposed model and it is compared with other methods PSO, GA, DE. The results of the methods have been tested through the standard IEEE 30 bus system. Based on numerical calculations and graphical representation, the optimal generation cost for OPF can be achieved.

A dynamic adaptive particle swarm optimization and genetic algorithm is presented to solve constrained engineering optimization problems. A dynamic adaptive inertia factor is introduced in the basic particle swarm optimization algorithm to balance the convergence rate and global optima search ability by adaptively adjusting searching velocity during search process. Genetic algorithm–related operators including a selection operator with time-varying selection probability, crossover operator, and n-point random mutation operator are incorporated in the particle swarm optimization algorithm to further exploit optimal solutions generated by the particle swarm optimization algorithm. These operators are used to diversify the swarm and prevent premature convergence. Tests on nine constrained mechanical engineering design optimization problems with different kinds of objective functions, constraints, and design variables in nature demonstrate the superiority of the dynamic adaptive particle swarm optimization and genetic algorithm against several other meta-heuristic algorithms in terms of solution quality, robustness, and convergence rate in most cases.

In this paper, a multi-objective hybrid firefly and particle swarm optimization (MOHFPSO) was proposed for different multi-objective optimal power flow (MOOPF) problems. Optimal power flow (OPF) was formulated as a non-linear problem with various objectives and constraints. Pareto optimal front was obtained by using non-dominated sorting and crowding distance methods. Finally, an optimal compromised solution was selected from the Pareto optimal set by applying an ideal distance minimization method. The efficiency of the proposed MOHFPSO technique was tested on standard IEEE 30-bus and IEEE 57-bus test systems with various conflicting objectives. Simulation results were also compared with non-dominated sorting based multi-objective particle swarm optimization (MOPSO) and different optimization algorithms reported in the current literature. The achieved results revealed the potential of the proposed algorithm for MOOPF problems.

Transmission network expansion planning (TNEP) is a key component of power system planning. Two important aspects to be considered in TNEP are economy and security factors, which are always contradictory and affect the benefits of both investment and power demand side, so the balance between these two factors needs to be focused. In this paper, a TNEP model based on coordinated economy and security is proposed so as to reconcile these two contradictory factors. The objective function of proposed TNEP model is to minimize the annual synthetic cost of transmission system, which contains investment cost, operation cost, maintenance cost and load shedding cost. The annual synthetic cost of transmission system is calculated with multistep load model established by load clustering using improved K-means algorithm. Then, an improved niche genetic algorithm is utilized to solve the TNEP model and an optimal scheme is obtained. Furthermore, the applicability and effectiveness of proposed TNEP model and algorithm are verified by case study on IEEE 24-bus system.

To solve the strong randomicity and slow convergence of the Particle Swarm Optimization(PSO) algorithms, two new particle’s position renewal formulas were analyzed on the basis of extrapolation in mathematics. A new modified PSO algorithms (called Leading PSO algorithms) was put forward. The Direct Torque Control(DTC) System was built in the environment of Matlab(Simulink). The weight and threshold values of BP Neural Network were trained using the modified PSO algorithms. Some disadvantages such as slow convergence speed and easily plunging into the local solution were avoided effectively. The simulation result shows that the system works well, and the rotor speed identifier has great static and dynamic performance.