Stochastic Economic Dispatch of a Power System With Solar Farm Considering Generation Flexibility and Reliability

IF 4.3 3区 工程技术 Q2 ENERGY & FUELS International Journal of Energy Research Pub Date : 2024-12-19 DOI:10.1155/2024/5528243
Asghar Sabzevari, Majid Moazzami, Bahador Fani, Ghazanfar Shahgholian, Mahnaz Hashemi
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Abstract

In this paper, a novel approach is presented for optimizing economic dispatch (ED) in power systems featuring solar farms and flexible loads. The ED problem is formulated as a multiobjective optimization task with stochastic characteristics. To address this challenge, a hybrid multiobjective algorithm named hybrid differential evolution algorithm (hDE)-multiobjective flower pollination algorithm (MOFPA) is proposed, which integrates principles from the differential evolution algorithm and the MOFPA, yielding enhanced performance. Two weighted objective functions are introduced to represent operation cost and the power system generation flexibility index (GFI) based on carefully selected scenarios. The occurrence probability of each scenario influences the objective function’s final value. Monte Carlo simulation (MCS) is utilized for scenario selection, enabling a comprehensive assessment of system performance. We first evaluate the proposed algorithm by optimizing standard benchmark functions and comparing results against those of other state-of-the-art algorithms. The outcomes demonstrate the superior accuracy and efficiency of the hDE-MOFPA algorithm. In the subsequent simulation phase, we optimally solve the ED problem, considering uncertainties and the involvement of flexible loads. A sensitivity analysis is conducted to examine the impact of uncertainties and flexible loads on both cost and emissions. The results reveal that the combined uncertainty of load and photovoltaic (PV) significantly influences the system. By adopting this novel approach, our proposed method offers valuable insights into optimizing the ED problem in power systems with solar farms and flexible loads, considering uncertainties and various scenarios. The sensitivity analysis indicates that considering uncertainties leads to a 4.9% increase in operational expenditures and a 4.1% decrease in GFI. Also, uncertainties in load and irradiation range from 5% to 20%, GFI experiences a decline from 4.08% to 20.18%, while costs undergo an increase from 4.92% to 18.93%.

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考虑发电灵活性和可靠性的太阳能电站电力系统的随机经济调度
本文提出了一种以太阳能发电场和柔性负荷为特征的电力系统经济调度优化的新方法。将ED问题表述为一个具有随机特征的多目标优化任务。为了解决这一问题,提出了一种混合多目标算法,即混合差分进化算法(hDE)-多目标花授粉算法(MOFPA),该算法将差分进化算法和MOFPA的原理相结合,提高了算法的性能。引入了两个加权目标函数,分别表示运行成本和发电系统柔性指数(GFI)。每个场景的发生概率影响目标函数的最终值。蒙特卡罗模拟(MCS)用于场景选择,实现对系统性能的全面评估。我们首先通过优化标准基准函数并将结果与其他最先进的算法进行比较来评估所提出的算法。结果表明,hDE-MOFPA算法具有较高的精度和效率。在后续的仿真阶段,考虑不确定性和柔性负载的介入,我们对ED问题进行了最优求解。对不确定性和柔性负荷对成本和排放的影响进行了敏感性分析。结果表明,负荷和光伏(PV)的组合不确定性对系统有显著影响。通过采用这种新颖的方法,我们提出的方法为优化具有太阳能发电场和灵活负载的电力系统中的ED问题提供了有价值的见解,考虑了不确定性和各种情况。敏感性分析表明,考虑不确定性导致运营支出增加4.9%,GFI减少4.1%。此外,负荷和辐照的不确定性在5% - 20%之间,GFI从4.08%下降到20.18%,而成本从4.92%增加到18.93%。
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来源期刊
International Journal of Energy Research
International Journal of Energy Research 工程技术-核科学技术
CiteScore
9.80
自引率
8.70%
发文量
1170
审稿时长
3.1 months
期刊介绍: The International Journal of Energy Research (IJER) is dedicated to providing a multidisciplinary, unique platform for researchers, scientists, engineers, technology developers, planners, and policy makers to present their research results and findings in a compelling manner on novel energy systems and applications. IJER covers the entire spectrum of energy from production to conversion, conservation, management, systems, technologies, etc. We encourage papers submissions aiming at better efficiency, cost improvements, more effective resource use, improved design and analysis, reduced environmental impact, and hence leading to better sustainability. IJER is concerned with the development and exploitation of both advanced traditional and new energy sources, systems, technologies and applications. Interdisciplinary subjects in the area of novel energy systems and applications are also encouraged. High-quality research papers are solicited in, but are not limited to, the following areas with innovative and novel contents: -Biofuels and alternatives -Carbon capturing and storage technologies -Clean coal technologies -Energy conversion, conservation and management -Energy storage -Energy systems -Hybrid/combined/integrated energy systems for multi-generation -Hydrogen energy and fuel cells -Hydrogen production technologies -Micro- and nano-energy systems and technologies -Nuclear energy -Renewable energies (e.g. geothermal, solar, wind, hydro, tidal, wave, biomass) -Smart energy system
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