Over the past half-century, Optimal Power Flow (OPF) has become one of the most important and widely studied nonlinear optimization problems. In general, OPF seeks to optimize the operation of electric power generation, transmission, and distribution networks subject to system constraints and control limits. Within this framework, however, there is an extremely wide variety of OPF formulations and solution methods. Moreover, the nature of OPF continues to evolve due to modern electricity markets and renewable resource integration. In this two-part survey, we survey both the classical and recent OPF literature in order to provide a sound context for the state of the art in OPF formulation and solution methods. The survey contributes a comprehensive discussion of specific optimization techniques that have been applied to OPF, with an emphasis on the advantages, disadvantages, and computational characteristics of each. Part I of the survey (this article) provides an introduction and surveys the deterministic optimization methods that have been applied to OPF. Part II of the survey examines the recent trend towards stochastic, or non-deterministic, search techniques and hybrid methods for OPF.

Optimal power flow: a bibliographic survey I

Over the past half-century, Optimal Power Flow (OPF) has become one of the most important and widely studied nonlinear optimization problems. In general, OPF seeks to optimize the operation of electric power generation, transmission, and distribution networks subject to system constraints and control limits. Within this framework, however, there is an extremely wide variety of OPF formulations and solution methods. Moreover, the nature of OPF continues to evolve due to modern electricity markets and renewable resource integration. In this two-part survey, we survey both the classical and recent OPF literature in order to provide a sound context for the state of the art in OPF formulation and solution methods. The survey contributes a comprehensive discussion of specific optimization techniques that have been applied to OPF, with an emphasis on the advantages, disadvantages, and computational characteristics of each. Part I of the survey provides an introduction and surveys the deterministic optimization methods that have been applied to OPF. Part II of the survey (this article) examines the recent trend towards stochastic, or non-deterministic, search techniques and hybrid methods for OPF.

Optimal power flow: A bibliographic survey II non-deterministic and hybrid methods

Multi-objective reactive power and voltage control based on fuzzy optimization strategy and fuzzy adaptive particle swarm

Due to the usage of high voltage direct current (HVDC) transmission links extensively in recent years, it requires more studies in this issue. Two-terminal HVDC transmission link is one of most important elements in electrical power systems. Generally, the representation of HVDC link is simplified for optimal reactive power flow (ORPF) studies in power systems. ORPF problem of purely AC power systems is defined as minimization of power loss under equality and inequality constraints. Hence, ORPF problem of integrated AC–DC power systems is extended to incorporate HVDC links taking into consideration power transfer control characteristics. In this paper, this problem is solved by genetic algorithm (GA) that is an evolutionary-based heuristic algorithm for the first time. The proposed method is tested on the modified IEEE 14-bus test system, the modified IEEE 30-bus test system, and the modified New England 39-bus test system. The validity, the efficiency, and effectiveness of the proposed method are shown by comparing the obtained results with that reported in literature. Thus, the impact of DC transmission links on the whole power systems is shown.

https://www.matlabi.ir/wp-content/uploads/bank_papers/g_paper/g163_Matlabi.ir_Optimizing%20reactive%20power%20flow%20of%20HVDC%20systems%20using%20genetic%20algorithm.pdf

Electrical Power and Energy Systems

Artificial bee colony (ABC) algorithm is an optimization algorithm based on the intelligent foraging behavior of honeybee swarm. Optimal reactive power flow (ORPF) based on ABC algorithm to minimize active power loss in power systems is studied in this paper. The advantage of ABC algorithm is that it does not require these parameters, because it is very difficult to determine external parameters such as cross over rate and mutation rate as in case of genetic algorithm and differential evolution. The other advantage is that global search ability of the algorithm is implemented by introducing a neighborhood source production mechanism which is similar to mutation process. Because of these features, ABC algorithm attracts much attention in recent years and has been used successfully in many areas. ORPF problem is one of these areas. In this paper, proposed algorithm is tested on both standard IEEE 30-bus test system and IEEE 118-bus test system. To show the effectiveness of proposed algorithms, the obtained results are compared with different approaches as available in the literature.

Artificial bee colony algorithm solution for optimal reactive power flow

Reactive power optimization in a power system solved by adjusting generator voltages, transformer taps and capacitors/reactors is a mixed integer nonlinear programming problem. GA (genetic algorithm), SA (simulated annealing) and TS (tabu search) are widely used in combinatorial optimization. Combining the advantages of individual algorithms, three GA/SA/TS hybrid algorithms to solve the reactive power optimization problem are proposed in this paper. Trying to reasonably combine local and global search, they adopt the acceptance probability of SA to improve the convergence of the simple GA, and apply tabu search to find more accurate solutions. Two power systems, the IEEE 30 bus system and a 125-bus practical area power system with 64 control variables in Shandong province, China, have been tested. Comparison results of the proposed algorithms with GA, SA/GA and TS show that the proposed GA/SA/TS hybrid method has the strongest capability of finding global optimal solution within reasonable computing time.

GA/SA/TS hybrid algorithms for reactive power optimization

Under the same environmental conditions, photovoltaic arrays at different operating voltages will significantly affect the size of solar power. To obtain as much power as possible, the maximum power point tracking (MPPT) control method has been invented. Previous studies focused on the tracking problem under local shadow conditions, but the dynamic shadow problem with rapid changes in local shadow was rarely discussed. To solve the tracking problem that the maximum power point has multi-peak and fast-changing characteristics under this condition, this paper proposes a simulated annealing MPPT control method based on particle swarm optimization, which compensates the disadvantages of the memoryless Markov chain in the optimization algorithm by adaptive asymmetric memory factor, thus improving the local search ability and convergence speed in the later stage. The control effect is verified by modeling and simulation in Simulink of MATLAB, and the adaptability of the heuristic control method is reflected by simulating the tracking mode under the moving shadow condition through the actual hardware deployment on the moving car.

/pdf/design-of-the-particle-swarm-optimization-simulated-1kxck0xt.pdf

Design of the particle swarm optimization simulated annealing MPPT control method based on motion shadow

According to the current lack of voltage sag application platform, this paper puts forward the development and application of voltage sag control platform. Firstly, the functional requirement analysis is carried out for the voltage sag pain point problem existing in provincial power grid companies. Secondly, the relationship between the application of provincial grid voltage sag platform and the existing system data is analyzed. Thirdly, the implementation path of the key technologies of each functional module developed by the voltage sag platform is analyzed and related research and development are carried out. Finally, through the example analysis of a provincial power grid, it is proved that the proposed platform can effectively analyze and apply existing monitoring data and simulation data, and realize the functions of voltage sag degree evaluation, risk prediction and auxiliary governance decision on the platform.

Development and Application of Voltage Sag Control Platform

Reactive power optimization by GA/SA/TS combined algorithms

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GA/SA/TS hybrid algorithms for reactive power optimization

Design of the particle swarm optimization simulated annealing MPPT control method based on motion shadow

Development and Application of Voltage Sag Control Platform

Reactive power optimization by GA/SA/TS combined algorithms