...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Energy Conversion & Management >A rule-based energy management scheme for long-term optimal capacity planning of grid-independent microgrid optimized by multi-objective grasshopper optimization algorithm
【24h】

A rule-based energy management scheme for long-term optimal capacity planning of grid-independent microgrid optimized by multi-objective grasshopper optimization algorithm

机译：基于规则的能量管理方案，通过多目标蚱蜢优化算法优化了基于网络无关的微电网的长期最佳容量规划

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Off-grid electrification of remote communities using sustainable energy systems (SESs) is a requisite for realizing sustainable development goals. Nonetheless, the capacity planning of the SESs is challenging as it needs to fulfil the fluctuating demand from a long-term perspective, in addition to the intermittency and unpredictable nature of renewable energy sources (RESs). Owing to the nonlinear and non-convex nature of the capacity planning problem, an efficient technique must be employed to achieve a cost-effective system. Existing techniques are, subject to some constraints on the derivability and continuity of the objective function, prone to premature convergence, computationally demanding, follows rigorous procedures to fine-tune the algorithm parameters in different applications, and often do not offer a fair balance during the exploitation and exploration phase of the optimization process. Furthermore, the literature review indicates that researchers often do not implement and examine the energy management scheme (EMS) of a microgrid while computing for the capacity planning problem of microgrids. This paper proposes a rule-based EMS (REMS) optimized by a nature-inspired grasshopper optimization algorithm (GOA) for long-term capacity planning of a grid-independent microgrid incorporating a wind turbine, a photovoltaic, a battery (BT) bank and a diesel generator (D-gen). In which, a rule-based algorithm is used to implement an EMS to prioritize the usage of RES and coordinate the power flow of the proposed microgrid components. Subsequently, an attempt is made to explore and confirm the efficiency of the proposed REMS incorporated with GOA. The ultimate goal of the objective function is to minimize the cost of energy (COE) and the deficiency of power supply probability (DPSP). The performance of the REMS is examined via a long-term simulation study to ascertain the REMS resiliency and to ensure the operating limit of the BT storage is not violated. The result of the GOA is compared with particle swarm optimization (PSO) and a cuckoo search algorithm (CSA). The simulation results indicate that the proposed technique's superiority is confirmed in terms of convergence to the optimal solution. The simulation results confirm that the proposed REMS has contributed to better adoption of a cleaner energy production system, as the scheme significantly reduces fuel consumption, CO2 emission and COE by 92.4%, 92.3% and 79.8%, respectively as compared to the conventional D-gen. The comparative evaluation of the algorithms shows that REMS-GOA yields a better result as it offers the least COE (objective function), at $0.3656/kW h, as compared to the REMS-CSA at $0.3662/kW h and REMSPSO at $0.3674/kW h, for the desired DPSP of 0%. Finally, sensitivity analysis is performed to highlight the effect of uncertainties on the system inputs that may arise in the future.
机译：使用可持续能源系统（Sess）的远程社区的离网电气是实现可持续发展目标的必要条件。尽管如此，除了可再生能源（RESS）的间歇性和不可预测性质之外，萨斯的能力规划是挑战的挑战，因为它需要从长远来看，从长远来看，即可再生能源（RESS）的间歇性和不可预测性。由于容量规划问题的非线性和非凸性性质，必须采用有效的技术来实现具有成本效益的系统。在对目标函数的衍生能力和连续性的某些约束的情况下，现有技术易于过早收敛，计算要求苛刻，遵循严格的程序来微调不同应用中的算法参数，并且通常不会在此期间提供公平的平衡优化过程的开发与探索阶段。此外，文献综述表明，研究人员通常不会在计算Microgrids的容量规划问题时不实现和检查微电网的能量管理方案（EMS）。本文提出了一种由自然启发蚱蜢优化算法（GOA）优化的基于规则的EMS（REMS），用于整合一个无关的微电网的长期容量规划，包括风力涡轮机，光伏，电池（BT）银行和柴油发电机（D-Gen）。其中，基于规则的算法用于实现EMS以优先考虑RES的用法并协调所提出的微电网组件的电力流。随后，尝试探索并确认拟议的REMS与果阿合并的效率。客观函数的最终目标是最大限度地减少能量（COE）的成本和供电概率（DPSP）的缺陷。通过长期仿真研究检查REM的性能，以确定REMS弹性，并确保不违反BT存储的操作限制。将GOA的结果与粒子群优化（PSO）和Cuckoo搜索算法（CSA）进行比较。仿真结果表明，在对最佳解决方案的收敛方面确认了所提出的技术的优势。仿真结果证实，拟议的REM有助于更好地采用更清洁的能源生产系统，因为与常规D-相比，该方案显着降低了92.4％，92.3％和79.8％的燃料消耗，二氧化碳排放和COE。根本。算法的比较评估表明，REMS-GOA产生更好的结果，因为它提供了最低COE（客观函数），与0.3656 / kWH H以0.3662美元/ kW H和REMSPSO为0.3674 / kW h，对于所需的DPSP为0％。最后，执行敏感性分析以突出显示未来可能产生的系统输入对不确定性的影响。

著录项

  • 来源
    《Energy Conversion & Management》 |2020年第10期|113161.1-113161.22|共22页
  • 作者

    Bukar Abba Lawan; Tan Chee Wei; Yiew Lau Kwan; Ayop Razman; Tan Wen-Shan;

  • 作者单位

    Univ Teknol Malaysia UTM Fac Engn Sch Elect Engn Div Elect Power Engn Skudai 81310 Johor Malaysia|Univ Maiduguri Fac Engn Dept Elect & Elect Engn PMB 1069 Maiduguri Borno State Nigeria;

    Univ Teknol Malaysia UTM Fac Engn Sch Elect Engn Div Elect Power Engn Skudai 81310 Johor Malaysia;

    Univ Teknol Malaysia UTM Fac Engn Sch Elect Engn Inst High Voltage & High Current Johor Baharu 81310 Malaysia;

    Univ Teknol Malaysia UTM Fac Engn Sch Elect Engn Div Elect Power Engn Skudai 81310 Johor Malaysia;

    Monash Univ Sch Engn Jalan Lagoon Bandar Sunway 47500 Selangor Malaysia|Monash Univ Adv Engn Platform Jalan Lagoon Bandar Sunway 47500 Selangor Malaysia;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Grasshopper optimization; Energy management scheme; Rule-based algorithm; PV; Metaheuristic algorithms; Pareto-optimal front;

    机译：蚱蜢优化;能源管理方案;基于规则的算法;光伏;综合算法;帕累托 - 最佳前线;

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号