Adopting Scenario-Based approach to solve optimal reactive power Dispatch problem with integration of wind and solar energy using improved Marine predator algorithm

The penetration of renewable energy resources into electric power networks has been increased considerably to reduce the dependence of conventional energy resources, reducing the generation cost and greenhouse emissions. The wind and photovoltaic (PV) based systems are the most applied technologies in electrical systems compared to other technologies of renewable energy resources. However, there are some complications and challenges to incorporating these resources due to their stochastic nature, intermittency, and variability of output powers. Therefore, solving the optimal reactive power dispatch (ORPD) problem with considering the uncertainties of renewable energy resources is a challenging task. Application of the Marine Predators Algorithm (MPA) for solving complex multimodal and non-linear problems such as ORPD under system uncertainties may cause entrapment into local optima and suffer from stagnation. The aim of this paper is to solve the ORPD problem under deterministic and probabilistic states of the system using an improved marine predator algorithm (IMPA). The IMPA is based on enhancing the exploitation phase of the conventional MPA. The proposed enhancement is based on updating the locations of the populations in spiral orientation around the sorted populations in the first iteration process, while in the final stage, the locations of the populations are updated their locations in adaptive steps closed to the best population only. The scenario-based approach is utilized for uncertainties representation where a set of scenarios are generated with the combination of uncertainties the load demands and power of the renewable resources. The proposed algorithm is validated and tested on the IEEE 30-bus system as well as the captured results are compared with those outcomes from the state-of-the-art algorithms. A computational study shows the superiority of the proposed algorithm over the other reported algorithms

Hybrid Bio-Inspired Computational Heuristic Paradigm for Integrated Load Dispatch Problems Involving Stochastic Wind

In this research work, bio-inspired computational heuristic algorithms (BCHAs) integrated with active-set algorithms (ASA) were designed to study integrated economics load dispatch problems with valve point effects involving stochastic wind power. These BCHAs are developed through variants of genetic algorithms based on a different set of routines for reproduction operators in order to make exploration and exploitation in the entire search space for finding the global optima, while the ASA is used for rapid local refinements of the results. The designed schemes are estimated on different load dispatch systems consisting of a combination of thermal generating units and wind power plants with and without valve point loading effects. The accuracy, convergence, robustness and complexity of the proposed schemes has been examined through comparative studies based on a sufficiently large number of independent trails and their statistical observations in terms of different performance indices

Design of Fractional Particle Swarm Optimization Gravitational Search Algorithm for Optimal Reactive Power Dispatch Problems

In fact, optimal RPD is one of the most critical optimization matters related to electrical power stability and operation. The minimization of overall real power losses is obtained by adjusting the power systems control variables, for instance; generator voltage, compensated reactive power and tap changing of the transformer. In this search, a new heuristic computing method named as fractional particle swarm optimization gravitational search algorithm (FPSOGSA) is presented by introducing fractional derivative of velocity term in standard optimization mechanism. The designed FPSOGSA is implemented for the optimal RPD problems with IEEE-30 and IEEE-57 standards by attaining the near finest outcome sets of control variables along with minimization of two fitness objectives; active power transmission line losses ( $P_{loss,}$ MW) and voltage deviation ( $\text{V}_{\mathrm {D}}$ ). The superior performance of the proposed FPSOGSA is verified for both single and multiple runs through comparative study with state of art counterparts for each scenario of optimal RPD problems

Optimal scheduling of short-term hydrothermal with integration of renewable energy resources using Lévy spiral flight artificial hummingbird algorithm

Short-term hydrothermal scheduling (STHS) is taking an important consideration in the power system for economics operation. The solution to the STHS problem gives time-varying scheduling of power generation with available hydro and thermal units aims to attain the cost saving of thermal units for a given time period. It is a non-convex optimization problem with a set of inequality and equality constrains such as load balancing, transmission losses as well as valve-point effect to the thermal power units. The permeation of renewable energy is frequently used into the power networks aims to reduce the cost of generation and emissions. The two foremost resources such as wind and photo-voltaic are frequently used in the power networks. In the research, the use of both powers integrated into the STHS problem to reduce the fuel cost ($) by using the modified artificial hummingbird algorithm (MAHA). To modelled the uncertainty in solar irradiance and windspeed, the lognormal and Weibull distribution are used. The novel MAHA is based on two enhancing methods; (1). updating the positions of the hummingbirds in stochastic motion via Lévy flight walk and (2). updating the location of the hummingbirds via pitch adjustment motion. The MAHA is further tested on the four STHS cases such as; four hydro one thermal (non-vple and vple), four hydro three thermal (vple without and with losses), four hydro three thermal with integration of RERs including direct cost only (VPLE without and with losses) and four hydro three thermal with uncertainty of RERs including direct, reverse and penalty costs (vple without losses), respectively. Moreover, for validation the performance of MAHA is further compared to the outcomes of AHA and other state-of-the-arts techniques