Ali Aamir, Aslam Sumbal, Keerio M U, Mirsaeidi Sohrab, Mugheri Noor Hussain, Ismail Muhammad, Abbas Ghulam, Othmen Salwa
Department of Electrical Engineering, Quaid-e-Awam University of Engineering Science and Technology, Nawabshah, Sindh, 67450, Pakistan.
School of Electrical Engineering, Beijing Jiaotong University, Beijing, 100044, China.
Heliyon. 2024 Oct 9;10(20):e39041. doi: 10.1016/j.heliyon.2024.e39041. eCollection 2024 Oct 30.
The Environmental Economic Power Dispatch (EEPD) problem, a widely studied bi-objective nonlinear optimization challenge in power systems, traditionally focuses on the economic dispatch of thermal generators without considering network security constraints. However, environmental sustainability necessitates reducing emissions and increasing the penetration of renewable energy sources (RES) into the electrical grid. The integration of high levels of RES, such as wind and solar PV, introduces stability issues due to their uncertain and intermittent nature. This paper addresses these concerns by formulating and solving the Stable Environmental Economic Power Dispatch (SEEPD) problem, which includes fixed zonal reserve capacity from conventional thermal generators and uncertain reserves from RES. Uncertainties in RES and load demand are modeled using random variable generation techniques, applying Gaussian, Weibull, and log-normal probability density functions (PDFs) for load demand, wind velocity, and solar irradiance, respectively. The stochastic SEEPD problem extends to multiple periods by replicating the single-period problem for each interval in the planning horizon, linking periods through intertemporal ramping costs, physical ramp rate, and fixed zonal reserve constraints on dispatch variables. Multi-Objective Evolutionary Algorithms (MOEAs) have gained importance for solving complex nonlinear problems involving multi-objective functions. This paper applies the latest MOEAs to tackle the proposed SEEPD problem, incorporating stochastic wind and solar PV power sources. Network security constraints, such as transmission line capacities and bus voltage limits, are considered along with constraints on generator capabilities and intertemporal spinning reserves, ramp-up and ramp-down constraints for thermal generators. A bidirectional coevolutionary-based multi-objective evolutionary algorithm is employed, integrating an advanced constraint-handling technique to ensure compliance with system constraints. The simulation results show that the proposed formulation achieves a better trade-off between various conflicting objective functions compared to other state-of-the-art MOEAs.
环境经济电力调度（EEPD）问题是电力系统中一个被广泛研究的双目标非线性优化难题，传统上它专注于热力发电机的经济调度，而不考虑网络安全约束。然而，环境可持续性要求减少排放并增加可再生能源（RES）在电网中的渗透率。诸如风能和太阳能光伏等高水平RES的整合，由于其不确定性和间歇性，会带来稳定性问题。本文通过制定和解决稳定环境经济电力调度（SEEPD）问题来解决这些问题，该问题包括传统热力发电机的固定区域备用容量和RES的不确定备用容量。RES和负荷需求的不确定性使用随机变量生成技术进行建模，分别对负荷需求、风速和太阳辐照度应用高斯、威布尔和对数正态概率密度函数（PDF）。通过在规划时域内对每个时段复制单时段问题，将随机SEEPD问题扩展到多个时段，通过跨时段爬坡成本、物理爬坡速率以及对调度变量的固定区域备用约束来连接各时段。多目标进化算法（MOEA）在解决涉及多目标函数的复杂非线性问题方面变得越来越重要。本文应用最新的MOEA来处理所提出的SEEPD问题，纳入了随机的风能和太阳能光伏电源。考虑了网络安全约束，如输电线路容量和母线电压限制，以及发电机能力约束、跨时段旋转备用、热力发电机的爬坡和降坡约束。采用了一种基于双向协同进化的多目标进化算法，集成了先进的约束处理技术以确保符合系统约束。仿真结果表明，与其他现有先进的MOEA相比，所提出的公式在各种相互冲突的目标函数之间实现了更好的权衡。
