1.题目和关键词
Title:
Multi-objective optimal design of hybrid renewable energy systems using preference-inspired coevolutionary approach
基于偏好启发协同进化方法的混合可再生能源系统的多目标优化设计
Keywords:
Hybrid renewable energy systems混合可再生能源系统；
Optimization优化；
Preference-inspired coevolutionary algorithm偏好启发的协同进化算法
2.摘要
As the increasing energy demand and rapid depletion of conventional fossil fuel resources, renewable energy has caused great attention of the public. The main drawback of the renewable resources is their unpredictable nature. A hybrid renewable energy system (HRES) that integrates different resources in proper combination is a promising solution to overcome this challenge. In this context, the preference-inspired coevolutionary algorithm (PICEA) has been applied for the first time to the design of multi-objective hybrid renewable energy system. We propose an enhanced fitness assignment method to improve the preference-inspired coevolutionary algorithm using goal vectors (PICEA-g) in the optimization process minimizing, simultaneously, the annualized cost of system (ACS), the loss of power supply probability (LPSP) and the fuel emissions. As an example of application, a stand-alone hybrid system including PV panels, wind turbines, batteries and diesel generators has been designed to find the best combination of components, achieving a set of non-dominated solutions from which the decision maker can select a most adequate one.
随着能源需求的增长和常规化石燃料资源的迅速枯竭，可再生能源引起了公众的极大关注。可再生资源的主要缺点是其不可预测性。混合可再生能源系统(HRES)将不同的资源以适当的方式整合在一起，是有希望克服这一挑战的解决方案。在此背景下，首次将偏好启发协同进化算法（PICEA）应用于多目标混合可再生能源系统的设计。我们提出了一种增强的适应度分配方法，以在优化过程中使用目标向量（PICEA-g）改进偏好启发式的协同进化算法，同时使系统年化成本（ACS）、供电损失概率（LPSP）和燃料排放量最小化。作为应用实例，我们设计了一个独立的混合系统，包括光伏板、风力涡轮机、电池和柴油发电机，以找到组件的最佳组合，实现一组非主导解决方案，决策者可从中选择最合适的解决方案。
3.创新点、学术价值
In this paper, a novel approach is presented for the optimal design of an HRES with diesel generators and battery storages. The modified preference-inspired coevolutionary algorithms using goal vectors (PICEA-ng) (Shi et al. 2014) is applied to minimize the annualized cost of the system (ACS), the loss of power supply probability (LPSP) and fuel emissions simultaneously. Preference-inspired coevolutionary algorithms using goal vectors (PICEA-g) (Wang et al., 2013) is an advanced search technique, and has the ability to attain better performance for multi-objective problems (especially many-objective problems) than other best-in-class MOEAs such as NSGA2 (Deb et al., 2002), SPEA2 (Zitzler et al., 2002) and MOEA/D. PICEA-ng is an enhanced version of the
PICEA-g where a new fitness assignment method is employed. The features of this approach are its high performance and simplicity compared with Pareto-based and decomposition based evolutionary algorithms. In addition, the number of optimization objectives is flexible to be extended and more renewable energy sources and storage devices can be considered easily.
本文提出了一种新的方法来优化设计带有柴油发电机和蓄电池的HRES（hybrid renewable energy system，混合可再生能源系统）。利用增强的适应度分配方法（简称 PICEA-ng)，使系统的年化成本(ACS)、供电损失概率(LPSP)和燃料排放同时最小化。使用目标向量的偏好启发协同进化算法（PICEA-g）（Wang等人，2013）是一种先进的搜索技术，对于多目标问题（尤其是高维目标问题）具有比其他同类最优的多目标进化算法（如NSGA2（Deb et al.，2002）、SPEA2（Zitzler et al.，2002）和MOEA/D获得更好的性能。PICEA-ng是PICEA-g的一个增强版本，它采用了一种新的适应度分配方法。与基于帕累托和基于分解的进化算法相比，该方法具有性能高、简单等特点。此外，优化目标的数量可灵活扩展，可方便地考虑更多的可再生能源和储存装置。
4.对结论的理解和对学习工作的启发
conclusion:
In this study, the preference-inspired coevolutionary algorithm (PICEA) has been applied for the first time to the design of multi-objective hybrid renewable energy system. We proposed an enhanced fitness assignment method to improve the preference-inspired coevolutionary algorithm using goal vectors (PICEA-g), followed by the simultaneous minimization of three objectives: the annualized cost of system (ACS), the loss of power supply probability (LPSP) and the fuel emissions. The main features of this approach are its high performance and simplicity compared with other evolutionary algorithms.
本文首次将偏好启发协同进化算法（PICEA）应用于多目标混合可再生能源系统的设计。我们提出了一种改进的适应度分配方法，用目标向量改进偏好启发的协同进化算法（PICEA-g），然后同时最小化三个目标：系统年化成本（ACS）、供电损失概率（LPSP）和燃料排放。与其它进化算法相比，该方法的主要特点是具有较高的性能和简单性。
A case study system composed of PV panels, wind turbines, batteries and diesel generators has been explained, in which the proposed method is evaluated and good optimal sizing performance is found. Except for the number of system components, the slope angle of PV panels and the height of wind turbines are also considered in the decisionvariables. Unlike single objective optimization, we obtained a large number of optimal sizing results, that is a set of non-dominated solutions, by the approach presented in this paper. The decision maker can select a most suitable solution from the set with specified preference information, studying further the objectives and the state variation of system components.
以由光伏板、风力发电机组、电池和柴油发电机组成的案例研究为例，对所提出的方法进行了评估，该方法具有良好的表现。除了系统组件的数量外，还考虑了光伏板的倾角和风力涡轮机的高度，与单目标优化不同，本文的方法得到了大量的优化结果，即一组非支配解。决策者可以从具有指定偏好信息的集合中选择最合适的解决方案，进一步研究系统各组成部分的目标和状态变化。
Future work:
For future research, a detailed hybrid system that takes the accessory components into account will be studied. Also, more objectives, e.g., unmet load and total system cost could be included into consideration. Lastly, uncertainties of the load and renewable energy sources availability will also be considered for more practical research.
为了将来的研究，将研究一个详细的混合系统，该系统将辅助组件考虑在内。另外，可以考虑更多的目标，例如未满足的负载和总的系统成本。最后，负荷和可再生能源可用性的不确定性也将被考虑用于更实际的研究。
