Model predictive economic/environmental dispatch of power systems with intermittent resources
Model predictive economic/environmental dispatch of power systems with intermittent resources
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DOI：
10.1109/pes.2009.5275940
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发表时间：
2009-07
期刊：
影响因子：
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通讯作者：
Le Xie;M. Ilić
中科院分区：
文献类型：
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作者：
Le Xie;M. Ilić
本文介绍了模型预测控制（MPC）在解决多间歇资源电力系统的多目标经济/环境调度问题中的潜在效益。基于对间歇资源下一短时间内（如5分钟）可用输出的预测模型，提出了一种以发电成本和环境成本均为目标函数最小为目标的发电资源调度的前瞻性最优控制算法。将该方法与(1)将间歇资源视为不确定负负荷的静态经济调度方法和(2)以发电总成本最小为单一目标函数的MPC调度方法进行了比较。我们发现，MPC方法可以通过直接调度可再生资源的发电机输出来降低发电成本，从而在预定义的时间范围内补偿时间负荷的变化。此外，多目标经济/环境成本函数为研究未来能源系统的效率和环境影响之间的权衡提供了一个公式。仿真是在一个包含5台发电机的12总线电力系统中实现的，以说明这种前瞻性调度对间歇性和更传统的发电厂的潜在好处。该方法可直接应用于具有大量风能和光伏资源的电力系统的管理。
This paper presents potential benefits of applying model predictive control (MPC) to solving the multi-objective economic/environmental dispatch problem in electric power systems with many intermittent resources. Based on the predictive model of the available output in the next short time period (e.g. 5 minutes) from the intermittent resources, this paper introduces a look-ahead optimal control algorithm for dispatching the available generation resources with the objective of minimizing objective function comprising both generation and environmental costs. This method is compared with (1) the static economic dispatch which treats intermittent resources as uncertain negative loads, and (2) the MPC dispatch with single objective function of minimizing the total generation cost. We show that the proposed MPC approach could lower the generation costs by directly dispatching the generator output from the renewable resources in order to compensate temporal load variations over pre-defined time horizon. Furthermore, the multi-objective economic/environmental cost function provides a formulation to study the tradeoff of efficiency and environmental impact in future energy systems. Simulation is implemented in a 12-bus power system comprising five generators to illustrate potential benefits from this look-ahead dispatch of both intermittent and more conventional power plants. The proposed method is directly applicable to managing power systems with large presence of wind and photovoltaic resources.