Optimal power flow solutions for power system operations using moth-flame optimization algorithm

Optimal power flow (OPF) has gained a growing attention from electrical power researchers since it is a significant tool that assists utilities of power system to determine the optimal economic and secure operation of the electric grid. The key OPF objective is to optimize a certain objective function such as: minimization of total fuel cost, emission, real power transmission loss, voltage deviation, etc. while fulfilling certain operation constraints like bus voltage, line capacity, generator capability and power flow balance. Optimal reactive power dispatch (ORPD) is a sub-problem of optimal power flow. ORPD has a considerable impact on the economic and the security of the electric power system operation and control. ORPD is considered a mixed nonlinear problem because it contains continuous and discrete control variables. Another sub-problem of OPF is Economic dispatch (ED) which one of the complex problems in the power system which its purposes is to determine the optimal allocation output of generator unit to satisfy the load demand at the minimum economic cost of generation while meeting the equality and inequality constraints. In this thesis, a recent metaheuristic nature-inspired optimization algorithm namely: Moth-Flame Optimizer (MFO) is applied to solve the two subproblems of Optimal power flow (OPF) namely: Economic dispatch (ED) and Optimal reactive power dispatch (ORPD) simultaneously. Three objective functions will be considered: generation cost minimization, transmission power loss minimization, and voltage deviation minimization using a weighted factor. The IEEE 30-bus test system and IEEE 57-bus test system will be employed, to investigate the effectiveness of the proposed MFO in solving the above-mentioned problems. Then the obtained MFO methods results is compared with other reported well-known methods. The comparison proves that MFO offers a better result compared to the other selected methods. In IEEE 30-bus test system, MFO outperform the other optimization methods with 967.589961$/h compared to 971.411400$/h, 983.738069$/h, 975.346233$/h, 985.198050$/h, 1035.537820$/h for Improved Grey Wolf Optimizer (IGWO), Grey Wolf Optimizer (GWO), Ant Loin Optimizer (ALO), Whale Optimization Algorithm (WOA), and Sine Cosine Algorithm (SCA) respectively. In IEEE 57-bus test system, MFO offers a minimization of 19.16% compared to 19.03% and 18.98% for Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA) respectively. Moreover, the MFO have speedy convergence rate and smooth curves more than the other algorithms

Optimal power flow using Hybrid Particle Swarm Optimization and Moth Flame Optimizer approach

In this study, the most common problem of the current power system named optimal power flow (OPF) is optimized using the recently hybrid meta-heuristic optimization technique Particle Swarm Optimization-Moth Flame Optimizer (PSO-MFO) algorithm. Hybrid PSO-MFO is an incorporation of PSO used for exploitation stage and MFO for exploration stage in an uncertain environment. The position and velocity of the particle are restructured according to Moth and flame location in each iteration. The hybrid PSO-MFO technique is carried out to solve the OPF problem. The performance of this technique is deliberated and evaluated on the standard IEEE 30-bus and IEEE 57-bus test system. The problems considered in the OPF are fuel cost reduction, Voltage stability enhancement and Active power loss minimization. The results obtained with hybrid PSO-MFO technique is compared with original PSO and MFO

Optimal Power Flow Solution With Stochastic Renewable Energies Using Nature Inspired Algorithm

The use of the Moth Flame Optimization (MFO) algorithm to solve optimal power flow as an objective optimization problem in power system operation and control is described in this thesis. Given the environmental consequences of pollution from fossil-fueled power plants, the optimal power flow that minimises only the overall cost of fuel appears to be no longer relevant as a single objective constraint. The optimization method, which is based on statistical models to solve optimal power flow and problems, shall be defined as a method for solving problems with a single identical objective function. Using the relevant equation, which is not violating the moth flame's system that has been developed as their base, the testing will run for a number of iterations, and after achieving the iterations, the testing will print out the output which is at their best optimal outcome, and this testing must run for a number of times to find the steady output for data collection. This method was tested on three different generation systems under varying load conditions. The results obtained using the proposed approach are comparable to those obtained using the other approaches discussed in the literature review. By the end of this study, this algorithm should have been demonstrated to be a process that is simple to use and capable of searching for a nearglobal optimal solution with significant convergence and effectiveness when compared to other algorithms

SLIME MOULD ALGORITHM FOR PRACTICAL OPTIMAL POWER FLOW SOLUTIONS INCORPORATING STOCHASTIC WIND POWER AND STATIC VAR COMPENSATOR DEVICE

Purpose. This paper proposes the application procedure of a new metaheuristic technique in a practical electrical power system to solve optimal power flow problems, this technique namely the slime mould algorithm (SMA) which is inspired by the swarming behavior and morphology of slime mould in nature. This study aims to test and verify the effectiveness of the proposed algorithm to get good solutions for optimal power flow problems by incorporating stochastic wind power generation and static VAR compensators devices. In this context, different cases are considered in order to minimize the total generation cost, reduction of active power losses as well as improving voltage profile. Methodology. The objective function of our problem is considered to be the minimum the total costs of conventional power generation and stochastic wind power generation with satisfying the power system constraints. The stochastic wind power function considers the penalty cost due to the underestimation and the reserve cost due to the overestimation of available wind power. In this work, the function of Weibull probability density is used to model and characterize the distributions of wind speed. Practical value. The proposed algorithm was examined on the IEEE-30 bus system and a large Algerian electrical test system with 114 buses. In the cases with the objective is to minimize the conventional power generation, the achieved results in both of the testing power systems showed that the slime mould algorithm performs better than other existing optimization techniques. Additionally, the achieved results with incorporating the wind power and static VAR compensator devices illustrate the effectiveness and performances of the proposed algorithm compared to the ant lion optimizer algorithm in terms of convergence to the global optimal solution.Мета. У статті пропонується процедура застосування нового метаеврістіческого методу в реальній електроенергетичній системі для розв’язання задач оптимального потоку енергії, а саме алгоритму слизової цвілі, який заснований на поведінці рою і морфології слизової цвілі в природі. Дане дослідження спрямоване на тестування і перевірку ефективності запропонованого алгоритму для отримання хороших рішень для проблем оптимального потоку потужності шляхом включення пристроїв стохастичною вітрової генерації і статичних компенсаторів VAR. У зв'язку з цим, розглядаються різні випадки, щоб мінімізувати загальну вартість генерації, знизити втрати активної потужності і поліпшити профіль напруги. Методологія. В якості цільової функції завдання розглядається мінімальна сукупна вартість традиційної генерації електроенергії і стохастичної вітрової генерації при задоволенні обмежень енергосистеми. Стохастична функція енергії вітру враховує величини штрафів через недооцінку і резервні витрати через завищену оцінку доступної вітрової енергії. У даній роботі функція щільності ймовірності Вейбулла використовується для моделювання і характеристики розподілів швидкості вітру. Практична цінність. Запропонований алгоритм був перевірений на системі шин IEEE-30 і великий алжирської тестовій енергосистемі зі 114 шинами. У випадках, коли мета полягає в тому, щоб звести до мінімуму традиційне вироблення електроенергії, досягнуті результати в обох тестових енергосистемах показали, що алгоритм слизової цвілі функціонує краще, ніж інші існуючі методи оптимізації. Крім того, досягнуті результати з використанням вітрової енергії і статичного компенсатора VAR ілюструють ефективність і продуктивність запропонованого алгоритму в порівнянні з алгоритмом оптимізатора мурашиних левів з точки зору збіжності до глобального оптимального рішення

Optimal power flow with distributed energy sources using whale optimization algorithm

Renewable energy generation is increasingly attractive since it is non-polluting and viable. Recently, the technical and economic performance of power system networks has been enhanced by integrating renewable energy sources (RES). This work focuses on the size of solar and wind production by replacing the thermal generation to decrease cost and losses on a big electrical power system. The Weibull and Lognormal probability density functions are used to calculate the deliverable power of wind and solar energy, to be integrated into the power system. Due to the uncertain and intermittent conditions of these sources, their integration complicates the optimal power flow problem. This paper proposes an optimal power flow (OPF) using the whale optimization algorithm (WOA), to solve for the stochastic wind and solar power integrated power system. In this paper, the ideal capacity of RES along with thermal generators has been determined by considering total generation cost as an objective function. The proposed methodology is tested on the IEEE-30 system to ensure its usefulness. Obtained results show the effectiveness of WOA when compared with other algorithms like non-dominated sorting genetic algorithm (NSGA-II), grey wolf optimization (GWO) and particle swarm optimization-GWO (PSO-GWO)

Hybrid moth flame optimization mppt algorithm for accurate real-time tracking under partially shaded photovoltaic system

Photovoltaic (PV) module is a packed solar cell, used for generating electricity from the sun’s energy. The application of PV power generation has gained its popularity with its easy implementation and inexhaustible energy resources. Due to the nonlinear characteristic of PV module, a maximum power point tracking (MPPT) is necessary to adjust the operating point based on the maximum power point (MPP) on the current-voltage (I-V) characteristic curve. However, under partial shaded conditions, the PV system is prone to local maxima problem and the challenge for MPPT increases, due to most of the commonly used MPPT algorithms were unable to track for the MPP effectively. To overcome the challenge, soft computing methods have been adapted in MPPT by researchers, with Particle Swarm Optimization (PSO) being the most prominent. However, the high computational power of PSO becomes a disadvantage in real-time and highly dynamic MPPT application. In addition, with the continuous improvement effort and the possibility of a new-comer algorithm can show superior results on the current problem, a new MFO based MPPT algorithm was proposed. In this study, a four-module 980 W solar PV system together with a DC/DC Boost Converter model was developed in MATLAB-Simulink as the MPPT algorithm test platform. Direct control strategy was adapted as the regulator for the DC/DC converter to replace the conventional proportional-integral (PI) controller to eliminate the need to tune the PI controller. Based on the study from MFO, it is found that the MFO was having the capability to perform effective tracking, despite its limitation of premature convergence problem near the MPP. To lift the limitation off, a new hybrid model named Hybrid MFO (HMFO) was proposed based on the combination of feature from MFO and conventional P&O, together with an additional partial shading detection feature. The performance of MFO and HMFO was compared with two well-established MPPT methods, namely Perturb and Observe (P&O) and PSO. To further evaluate the real-time performance of the MPPT algorithms, hardware-in-the-loop (HIL) was utilized to emulate the behavior of the PV system and power converter while a digital signal processor (DSP) was used to implement the MPPT algorithms in study. All four MPPT methods were simulated and real-time evaluated under 10 constant irradiance test cases, 30 dynamic irradiance test cases and 100 partial shaded irradiance test cases. HMFO has shown fast tracking and achieved the highest average efficiency among the soft computing methods under constant irradiance conditions. Under dynamic irradiance condition, HMFO was able to reach the new MPP faster and more effective than both PSO and MFO. Under partial shaded conditions, the HMFO was able to show the highest average tracking efficiency and the fastest convergence time among the soft computing method. The HMFO was able to track for true MPP for about three times more than the P&O under partial shaded conditions and it was able to achieve the average tracking efficiency up to 99.35 %

Economic power dispatch solutions incorporating stochastic wind power generators by moth flow optimizer

Optimization encourages the economical and efficient operation of the electrical system. Most power system problems are nonlinear and nonconvex, and they frequently ask for the optimization of two or more diametrically opposed objectives. The numerical optimization revolution led to the introduction of numerous evolutionary algorithms (EAs). Most of these methods sidestep the problems of early convergence by searching the universe for the ideal. Because the field of EA is evolving, it may be necessary to reevaluate the usage of new algorithms to solve optimization problems involving power systems. The introduction of renewable energy sources into the smart grid of the present enables the emergence of novel optimization problems with an abundance of new variables. This study's primary purpose is to apply state-of-the-art variations of the differential evolution (DE) algorithm for single-objective optimization and selected evolutionary algorithms for multi-objective optimization issues in power systems. In this investigation, we employ the recently created metaheuristic algorithm known as the moth flow optimizer (MFO), which allows us to answer all five of the optimal power flow (OPF) difficulty objective functions: (1) reducing the cost of power generation (including stochastic solar and thermal power generation), (2) diminished power, (3) voltage variation, (4) emissions, and (5) reducing both the cost of power generating and emissions. Compared to the lowest figures provided by comparable approaches, MFO's cost of power production for IEEE-30 and IEEE-57 bus architectures is $888.7248 per hour and$ 31121.85 per hour, respectively. This results in hourly cost savings between 1.23 % and 1.92%. According to the facts, MFO is superior to the other algorithms and might be utilized to address the OPF problem

OPTIMAL POWER MANAGEMENT OF DGS AND DSTATCOM USING IMPROVED ALI BABA AND THE FORTY THIEVES OPTIMIZER

In this study an improved Ali Baba and the forty thieves Optimizer (IAFT) is proposed and successfully adapted and applied to enhance the technical performances of radial distribution network (RDN). The standard AFT governed by two sensible parameters to balance the exploration and the exploitation stages. In the proposed variant a modification is introduced using sine and cosine functions to create flexible balance between Intensification and diversification during search process. The proposed variant namely IAFT applied to solve various single and combined objective functions such as the improvement of total power losses (TPL), the minimization of total voltage deviation and the maximization of the loading capacity (LC) under fixed load and considering the random aspect of loads. The exchange of active powers is elaborated by integration of multi distribution generation based photovoltaic systems (PV), otherwise the optimal management of reactive power is achieved by the installation of multi DSTATCOM. The efficiency and robustness of the proposed variant validated on two RDN, the 33-Bus and the 69-Bus. The qualities of objective functions achieved and the statistical analysis elaborated compared to results achieved using several recent metaheuristic methods demonstrate the competitive aspect of the proposed IAFT in solving with accuracy various practical problems related to optimal power management of RDN