Search

In the current era of industrialization, renewable energy (such as wind and solar energy) plays an significant role in power generation sector, and it helps to decrease the generation cost and the environmental pollution. In this work, hybrid source of energy (like solar and wind energy) has been integrated with the traditional dynamic economic emission dispatch (EED) (DEED) problem to formulate a new model, i.e., complicated constrained hybrid energy integrated DEED problem, which may be able to generate less polluted power than the traditional DEED problem. Here, the quasi-opposition learning (QOL) approach and chaotic dynamics have been introduced in a novel sine cosine algorithm (SCA) to enhance its convergence and diversity. Effectiveness of the presented solution of the dynamic thermal-wind-solar EED problem has been properly validated by solving three aspects, i.e., the cost, the emission and the combined cost-emission using QOL-based chaotic SCA. The use of solar and wind energy in the DEED problem reduces the generating costs by 8% and 20.84%, respectively, compared to traditional and wind-integrated DEED. The robustness of the proposed modified SCA has been presented by comparing the results with the results offered by SCA and other recently published algorithms. [ABSTRACT FROM AUTHOR]

In the upcoming decades, renewable energy will find significant usage in power industries. Renewable energy while supplementing conventional energy may experience a huge problem during making of control strategy. Automatic generation control executes an imperative contribution in power system operation to improve its frequency deviation within a specified range. Initially, a single-area thermal system is considered to design a non-fragile proportional-integral-derivative (NFZ-PID) controller. To analyze the stability of the system, Routh– Kharitonov theorem is taken into account during the optimization operation to ensure system stability and system response. This experiment has been carried out by a popular optimization technique such as chaotic opposition krill herd algorithm. However, the main objective of this article is to design a NFZ-PID controller as new control strategy for the said thermal-solar-wind system under deregulated scenario. The proposed NFZ-PID controller assures that frequency deviation and tie-line power under step load disturbances is settled down to zero in minimum definite time. Moreover, robustness test is performed to validate controller's robustness at large changes in system parameters, under RLP type of load. [ABSTRACT FROM AUTHOR]

In recent years, many studies have been used in feed-forward neural network (FNN) to develop decision-making systems. The primary objective is to get the least error by finding the best combination of control parameters. It has been observed that FNNs using meta-heuristics techniques always converges very quickly towards the optimal positions but suffers from slow searching speeds at later stages of generation. Due to slow convergence, it is a prevalent phenomenon that traditional optimization does not ensure to find global optima. As a result, it falls under local optima. Recently, another meta-heuristic optimization-based algorithm called sine cosine algorithms (SCA) was introduced to solve the aforementioned issues. The algorithm is fundamentally predicated on two trigonometric functions, one being sine and the other being cosine. However, like other traditional approaches, SCA has a tendency to be stuck in sub-optimal regions due to poor exploration and exploitation capabilities. This paper proposes an improved version of SCA named chaotic oppositional SCA (COSCA) by integrating with chaos theory and oppositional based learning into the SCA optimization process. It is an incipient training method employed to train an FNN. Three benchmark problems are used to examine the precision and performance of FNNs equipped with COSCA, COPSO, OSCA, SCA, PSO, and backpropagation. The experimental results showed that, relative to other meta-heuristic optimization techniques, the COSCA technique is able to improve performance. [ABSTRACT FROM AUTHOR]

Renewable energy‐based hydro‐thermal scheduling is a new assignment in solar‐wind‐hydro power structures including thermal plants with non‐convex fuel costs, a time delay of the multi‐reservoir cascaded hydro unit, generating units for wind power, and photo‐voltaic plant of the solar system. Renewable energy resources are used in immense quantity as they are naturally accessible and charge‐free. In this regard, this article presents a single‐objective economic replica of short‐term hydro‐thermal scheduling (HTS) problems having renewable solar and wind units. To speed up the convergence swiftness, of OBL is incorporated with the fundamental grasshopper optimization algorithm (GOA) method which is actively associated with the social communication of the grasshopper in the environment. Furthermore, HTS and hydro thermal scheduling incorporating solar and wind energy are considered for the benchmark test systems. Results presented by a few recent techniques (like fuzzy based evolutionary programming, teaching learning‐based optimization, etc.) have been compared with those obtained by the oppositional GOA (OGOA) to set up its effectiveness. Simulation results of OGOA technique clearly show that the renewable solar and wind units can significantly reduce the fuel cost of the power systems. [ABSTRACT FROM AUTHOR]

Source of fossil fuel is impoverishing in the upcoming future. Renewable energy sources (RESs) are becoming challenging conventional energy substitutes in the present scenario. In this article, an attempt has been made to utilize RESs such as wind and solar energy with combined heat and power economic dispatch problems. The intention of this presentation is to minimize conflict objectives such as fuel cost accomplished with load demand along with transmission losses while satisfying all the constraints. A new optimization technique, namely a quasi‐oppositionalbased whale optimization algorithm (QOWOA) is adopted to cope up with the non‐linearities of the chosen systems. The proposed technique is tested on two different nonlinear realistic power systems to achieve the satisfactory performances. The superiority of the proposed QOWOA algorithm is judged by comparing it with some recently developed metaheuristic optimization techniques. [ABSTRACT FROM AUTHOR]