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首页> 外文期刊>Australian Journal of Electrical and Electronics Engineering >Advanced hybrid intelligent model and algorithm for de-regulated electricity market
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Advanced hybrid intelligent model and algorithm for de-regulated electricity market

机译：先进的混合智能模型和解压力电力市场算法

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ABSTRACT An advanced aggressive framework is developed in diverse electricity markets all over the world, which has altered the manner that electric companies yield benefits. Under such circumstance, the companies are supposed to implement required bidding models not only for the sake of attaining reasonable dispatch but also for enhancing benefits. Since the artificial intelligence has found successful in many applications, this paper intends to deploy the hybrid model with the combination of Group Search Optimisation (GSO) and Gravitational Search Algorithm (GSA) termed as Group Search with Gravitational Force (GSGF) model to meet demand-side management principles. By considering the parameters under deregulated environment, the proposed model solves the unit commitment problem. Accordingly, this paper designs the bidding model of IEEE-30 and IEEE-75 test bus system with appropriate bidding coefficient, which appears to attain a high profit. Further, it analyses the performance of both the test bus systems by evaluating total profit, consumed bidding power, statistical report of cost and Market Clearing Price (MCP). In the analysis, it compares the performance of proposed GSGF model with the traditional GA (Genetic Algorithm), ABC (Artificial Bee Colony), PSO (Particle Swarm Optimisation), GSA and GSO bidding models. From the experimental results, the profit of the proposed model is higher than the conventional model, thus attains maximum performance.
机译：摘要在全球各地的不同电力市场开发了一个先进的攻击框架，这改变了电力公司产生了福利的方式。在这种情况下，这些公司应该不仅为了获得合理的派遣而实施所需的招标模式，而且为了提高福利。由于人工智能在许多应用中找到了成功，因此本文打算将混合模型部署，与Group搜索优化（GSO）和引力搜索算法（GSA）的组合作为Group Search称为Greativation Force（GSGF）模型以满足需求 - 方向管理原则。通过考虑解除管制环境下的参数，所提出的模型解决了单位承诺问题。因此，本文设计了IEEE-30和IEEE-75测试总线系统的招标模型，具有适当的竞标系数，似乎达到了高利润。此外，它通过评估总利润，消耗的招标电力，成本和市场清算价格（MCP）的统计报告来分析测试总线系统的性能。在分析中，它比较了GSGF模型与传统GA（遗传算法），ABC（人造蜂殖民地），PSO（粒子群优化），GSA和GSO竞标模型的性能。从实验结果来看，所提出的模型的利润高于传统模型，从而达到最大性能。

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