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The primary objective of this article is the design and development of the optimal hybrid renewable energy (HRE) system for rural communities through village‐owned resources. The optimal performance and economic analysis is carried out with a suitable multi‐energy generation configuration model simulation. Based on the simulation outcomes the feasibility report of the proposed HRE system is to be considered for the investor's decision‐making process. The complete analysis has been planned with three important phases such as pre assessment stage, simulation and optimization stage, and evaluation stages. This real conducted case study has expressed many interesting facts about the integration of renewable resources toward meeting the rural energy requirements with economical and smooth manners. The proposed configuration is capable to generate net annual energy of 89,822 kWh/year with CoE (cost of energy) of Rs. 8.102. In addition, the proposed HRE system is reducing the CO2 impact on the environment by nearly 3.5% with an excess energy capacity of 6688 kWh/year. [ABSTRACT FROM AUTHOR]

This paper presents a new approach to solving the short-term unit commitment problem using an evolutionary programming-based tabu search (TS) method. The objective of this paper is to find the generation scheduling such that the total operating cost can be minimized, when subjected to a variety of constraints. This also means that it is desirable to find the optimal generating unit commitment in the power system for the next H hours. Evolutionary programming, which happens to be a global optimization technique for solving unit commitment problem, operates on a system, which is designed to encode each unit's operating schedule with regard to its minimum up/down time. In this, the unit commitment schedule is coded as a string of symbols. An initial population of parent solutions is generated at random. Here, each schedule is formed by committing all of the units according to their initial status ('flat start'). Here, the parents are obtained from a predefined set of solutions (i.e., each and every solution is adjusted to meet the requirements). Then, a random decommitment is carried out with respect to the unit's minimum downtimes, and TS improves the status by avoiding entrapment in local minima. The best population is selected by evolutionary strategy. The Neyveli Thermal Power Station (NTPS) Unit-II in India demonstrates the effectiveness of the proposed approach; extensive studies have also been performed for different power systems consisting of 10, 26, and 34 generating units. Numerical results are shown comparing the cost solutions and computation time obtained by using the evolutionary programming method and other conventional methods like dynamic programming, Lagrangian relaxation, and simulated annealing and tabu search in reaching proper unit commitment. Index Terms--Dynamic programming, evolutionary programming, Lagrangian relaxation, Tabu search, unit commitment.

Byline: C. Christober Asir Rajan This paper presents a new approach to solve the short-term unit commitment problem using genetic algorithm-based simulated annealing method for utility system. The objective of this paper is to find the generation scheduling such that the total operating cost can be minimised when subjected to a variety of constraints. This also means that it is desirable to find the optimal generating unit commitment in the power system for the next H hours. Genetic Algorithms (GAs) are general-purpose optimisation techniques based on principles inspired from the biological evolution using metaphors of mechanisms such as neural section, genetic recombination and survival of the fittest. In this, the unit commitment schedule is coded as a string of symbols. An initial population of parent solutions is generated at random. Here, each schedule is formed by committing all the units according to their initial status ('flat start'). Here, the parents are obtained from a predefined set of solutions, i.e., each and every solution is adjusted to meet the requirements. Then, a random recommitment is carried out with respect to the unit's minimum downtimes. Simulated Annealing (SA) improves the status. A 66-bus utility power system with 12 generating units in India demonstrates the effectiveness of the proposed approach; extensive studies have also been performed for different IEEE test systems consisting of 24, 57 and 175 buses. Numerical results are shown comparing the cost solutions and computation time obtained by using the GA method and other conventional methods.

Byline: C. Christober Asir Rajan, M.R. Mohan This paper presents a new approach to solving short-term Unit Commitment Problem (UCP) using Neural-Based Tabu Search (NBTS) for utility system. The objective of this paper is to find the generation scheduling such that the total operating cost can be minimised, when subjected to a variety of constraints. This also means that it is desirable to find the optimal generating unit commitment in the power system for next H hours. A 66-bus utility power system with 12 generating units in India demonstrates the effectiveness of the proposed approach; extensive studies have also been performed for different IEEE test systems consisting of 24, 57 and 175 buses. Numerical results are shown to compare the superiority of the cost solutions obtained using the Tabu Search method, Dynamic Programming and Lagrangian Relaxation methods in reaching the proper unit commitment.