Search

Electricity has become one of the most essential components of establishing a quality standard of living in any country. Consequently, considerable work has been focused on designing a sophisticated load frequency control (LFC) system. However, in light of limited resources and real-world challenges, computationally based control algorithms that are more effective and less expensive remain critically needed. Thus, this paper employs a modified honey badger algorithm (HBA) in conjunction with the concepts of Lévy flight and inertia weight to optimize the parameters of a new cascaded two-degree-of-freedom fractional-PID structure coupled with a proportional derivative (2DOF + FOPIDN)-PD controller to solve LFC problems in an interconnected power system (IPS) comprising conventional and renewable energy sources (RES). The proposed control technique is applied to a two-area IPS under diverse load conditions and in the presence of nonlinear elements and electronic devices. The proposed method is evaluated with respect to a range of performance metrics, such as settling time, undershoots, and error criteria values. The collective performance of the established control scheme indicated that the suggested control approach provides excellent reliability under various load condition scenarios, sensitivity tests, and perturbations, proving the system's efficacy and dependability. [ABSTRACT FROM AUTHOR]

Distributed generation (DG) plays a vital role in electrical power networks. However, power loss reduction, voltage profile improvement, friendly environment, and reliability are all benefits of DG units. In this research work, a worthwhile methodology is recommended for optimal allocation of traditional (gas turbine) and renewable energy sources that are based on distributed generators which include solar and wind in the distribution system. The major objective of the research paper is the minimization of real, reactive power losses and emissions produced during the application of these conventional sources. Originally, the best locations to place this DG are identified using the concept of water, energy, and food algorithm (WEFA). The number and sizes of these renewable energy sources selected (wind and solar) are determined by applying the concepts of the Dragonfly Algorithm. The Weibull and beta distribution functions are modeled to extract the exact position to fix our DGs to minimize losses within the distribution network. To assess the performance of WEF five different cases scenario considered are DG capacity, Location of Bus, voltage profile, maximum power loss as well as utilization rate. The proposed WEF Algorithm is tested on the IEEE standard 33-bus system. The simulated results were compared with others found in literature and found to be better in terms of power loss reductions. [ABSTRACT FROM AUTHOR]

• This paper presents the Survey of AI Methods for Renewable Energy Forecasting. • Taxonomy of AI for solar and wind renewable energy is presented. • Combinations of ML and metaheuristic methods for renewable energy are discussed. • Prospects of improved metaheuristic algorithms for renewable energy forecasting are presented. The efforts to revolutionize electric power generation and produce clean and sustainable electricity have led to the exploration of renewable energy systems (RES). This form of energy is replenished and cost-effective in terms of production and maintenance. However, RES, such as solar and wind energies, is intermittent; this is one of the drawbacks of its usage. In order to overcome this limitation, studies have been undertaken to forecast its availability and power output. The current trending method of forecasting availability and the power generated by the RES is the artificial intelligence (AI) method. However, with all its potential, traditional AI, such as Artificial Neural Network (ANN), Support Vector Machine (SVM) and many more, does not have it all. Because of this, metaheuristic algorithms are being explored as optimization techniques to increase the performance accuracy of these AI forecasting methods and overcome some challenges of traditional AI models. This study presents an insightful survey of AI (traditional AI and metaheuristic) methods explored to forecast the availability of solar and wind renewable energy systems. A survey of the existing surveyed literature was presented. The taxonomy of the explored AI in RES was formulated, and the theoretical backgrounds of some of the traditional AI algorithms were presented. Also, the various forms of metaheuristic algorithms and the improved versions applied to optimize classical AI methods to forecast RES systems' availability and power output were surveyed. A conceptual framework of the hybrid AI application in RES was formulated. Finally, the survey discussion, insight, challenges of the existing models and future directions were presented. [ABSTRACT FROM AUTHOR]

Numerous potential advantages to the requirements and effectiveness of the supplied electricity can be accomplished by the installation of distributed generation units. In order to take full advantage of these benefits, it is essential to position the Distributed Generation (DG) units in appropriate locations. Otherwise, their installation may have an adverse effect on the quality of energy and system operation. Several optimization techniques have been created over the years to optimize distributed generation integration. Optimization techniques are therefore constantly changing and have been the main attention of many fresh types of research lately. This article evaluates cutting-edge techniques of optimizing the issue of positioning and sizing distributed generation units from renewable energy sources based on recent papers that have already been applied to distribution system optimization. Furthermore, this article pointed out the environmental, economic, technological and regulatory drivers that lead to a rapid interest in the DG system based on renewable sources. A summary of popular meta-heuristic optimization tools discussed in table form with merits and demerits to increase fresh prospective paths to multi-approach that have not yet been studied. [ABSTRACT FROM AUTHOR]

The increase in the price of fossil fuel due to its rarity and emissions means more integration of renewable energy sources (RESs) is required to improve economic management of the grid. This study analyses a combination of the tie‐line power between conventional power sources and the renewable energy system and its frequency deviations which are known as area control error. The minimisation of the tie‐line by optimal control is affected in such a way that the frequency and tie‐line power error can be minimised while maintaining the power balance between generation and load. The tie‐line connected to the microgrid consists of two main parts, namely the conventional source and the renewable energy source, each made up of a synchronous generator. The control application of the active power and frequency to a network is referred to as load frequency control (LFC) with the storage system as an integral part of RES. The simulation results show the performance of the proposed optimal control model in microgrid during the changing loads' condition, where the energy storage system applied to optimal control has shown a quick response to frequency deviation, which is close to 80%. [ABSTRACT FROM AUTHOR]

In composite expansion planning, improving the efficiency and power quality of power supplied from Renewable Energy Sources (RES) is highly essential. Therefore, this paper focuses on developing an optimal power flow optimization model for minimizing power loss and harmonics emanating from RES with emphasis on reducing their impact on the Locational Marginal Prices. In order to assess the magnitude of harmonics, grid modelling and simulation with RES such as wind and solar energy were carried out using the Electrical Transient Analyzer Program 12.6.0 software. The proposed mixed integer nonlinear programming mathematical model was solved using the Outer Approximation Algorithm solver in the advanced interactive multidimensional modelling system, and the efficiency of the model was tested on the modified IEEE 6 bus and 24 bus system. The results obtained from simulations reveal the efficiency of the proposed model in terms of effective cost reduction, nodal marginal price reduction, minimization of harmonic losses, and additional lines in the power system network. [ABSTRACT FROM AUTHOR]

The uncertainty introduced by intermittent renewable energy generation and prosumer energy imports makes operational planning of renewable energy-assisted prosumer microgrids challenging. This is due to the difficulty in obtaining accurate forecasts of the energy expected from these renewable energy sources and prosumers. Operators of such microgrids therefore require additional grid-balancing tools to maintain power supply and demand balance during grid operation. In this paper, the impact of demand response aggregators (DRAs) in a prosumer microgrid is investigated. This is achieved by developing and solving a deterministic mathematical formulation for the operational planning of the grid. Also, taking a cue from CAISO's proposed tariff revision which allows the state-of-charge of non-generator resources (like storage units) to be submitted as a bid parameter in the day-ahead market and permits scheduling coordinators of these resources to self-manage their energy limits and state-of-charge, the proposed formulation permits prosumers to submit the battery energy content as a bid parameter and self-manage their battery energy limits. Furthermore, a robust counterpart of the model is developed. Both formulations are constrained mixed integer optimization problems which are solved using the CPLEX solver in the Advanced Interactive Multidimensional Modelling System environment. The results obtained from tests carried out on a hypothetical prosumer microgrid show that the operating cost of the microgrid reduces in the presence of DRAs. In addition, the storage facility owner may benefit from self-managing its energy limits, but this may cut the amount of grid-balancing resource available to the microgrid operator, thereby increasing the operating cost of the microgrid. [ABSTRACT FROM AUTHOR]

In recent times, Microgrids (MG) have emerged as solution approach to establishing resilient power systems. However, the integration of Renewable Energy Resources (RERs) comes with a high degree of uncertainties due to heavy dependency on weather conditions. Hence, improper modeling of these uncertainties can have adverse effects on the performance of the microgrid operations. Due to this effect, more advanced algorithms need to be explored to create stability in MGs'. The Model Predictive Control (MPC) technique has gained sound recognition due to its flexibility in executing controls and speed of processors. Thus, in this review paper, the superiority of MPC to several techniques used to model uncertainties is presented for both grid-connected and islanded system. It highlights the features, strengths and incompetencies of several modeling methods for MPCs and some of its variants regarding handling of uncertainties in MGs. This survey article will help researchers and model developers to come up with more robust model predictive control algorithms and techniques to cope with the changing nature of modern energy systems, especially with the increasing level of RERs penetration. [ABSTRACT FROM AUTHOR]