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Coordination of combined heat and power-thermal-wind-photovoltaic units in economic load dispatch using chance-constrained and jointly distributed random variables methods

机译:机会约束联合分布随机变量方法在经济负荷调度中热电联产

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CHP (Combined heat and power) generation or cogeneration has been considered worldwide as the major alternative to traditional systems in terms of significant energy saving and environmental conservation. Furthermore, the wind power generators and photovoltaic units have vastly speared over the power systems due to their free inputs. However, there are many challenges for power system operators because of uncertain characteristics of renewable units and load demands. Therefore, a new multi-objective stochastic framework based on chance constrained programming is developed to handle combined heat and power economic load dispatch considering the stochastic characteristics of wind and photovoltaic power outputs, customer's electrical and heat load demands. The proposed technique makes use of a jointly distributed random variables method to calculate chance of meeting the electrical and heat load requirement using the target decision variables while maintaining the electrical energy cost below a scheduled value. The framework benefits from a new method named hybrid modified cuckoo search algorithm and differential evolution to extract the Pareto optimal surface for minimum cost and maximum probability of meeting the target cost and applies them as the objective functions. Applying to 6 and 40 unit test systems, the ability of the suggested framework is confirmed. (C) 2014 Elsevier Ltd. All rights reserved.
机译:就显着的节能和环境保护而言,CHP(热电联产)发电或热电联产在世界范围内被视为传统系统的主要替代产品。此外,由于它们的自由输入,风力发电机和光伏单元已经在电力系统上大量普及。然而,由于可再生单元的特性不确定和负载需求,电力系统运营商面临许多挑战。因此,基于风能和光伏电力输出的随机特性,客户的电力和热负荷需求,开发了一种基于机会约束编程的新的多目标随机框架,以处理热电联产经济负荷调度。所提出的技术利用联合分布的随机变量方法来计算使用目标决策变量满足电力和热负荷要求的机会,同时将电能成本保持在预定值以下。该框架得益于一种名为混合改进的杜鹃搜索算法和差分进化的新方法,该方法可提取帕累托最优曲面,以最小成本和最大概率满足目标成本,并将其用作目标函数。适用于6和40单元测试系统,证实了建议框架的功能。 (C)2014 Elsevier Ltd.保留所有权利。

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