Corresponding author’s email address: zainabobaone@gmail.com 1039 ARID ZONE JOURNAL OF ENGINEERING, TECHNOLOGY & ENVIRONMENT REVIEW ARTICLE RECENT TRENDS IN THE OPTIMAL RENEWABLE POWER DISPATCH FRAMEWORKS IN RENEWABLE ENERGY-RELIANT POWER SYSTEMS Z. Oba-Sulaiman*, S. O. Adetona, A. S. Alayande, A. Balogun Department of Electrical and Electronics Engineering, University of Lagos, Nigeria *Corresponding author’s email: zainabobaone@gmail.com ARTICLE INFORMATION ABSTRACT This study presents a comprehensive survey of Optimal Renewable Power Dispatch (ORPD) frameworks in grid-rich renewables. The work employs comparative, and trend approaches to analyze optimization methods for ORPD of 61 relevant studies published from 2018 to 2024 by making use seven parameters emerged from the overall studies include voltage stability, carbon footprint, active power, reactive power, power loss, congestion, and energy storage. The results were tabulated using bibliometric tables, trend analysis, charts, and figures. The results show that previous research mainly emphasized minimizing economic cost through active power and loss optimization. However, voltage stability and congestion constraints were rarely integrated into the optimization framework simultaneously. Despite this, most studies remain focused on tackling these issues individually, where voltage stability is usually treated as a minor constraint, loss minimization is framed as a separate objective, and congestion management is approached through standalone mechanisms. While some integrated frameworks exist, they often address a limited scope of objectives, resulting in omissions in the development of comprehensive models. To close this significant research gap, there is need for the development of robust, multi-objective, and computationally efficient frameworks that holistically address these intertwined issues. The findings will therefore have academic, technical, and practical relevance, offering both theoretical contributions and actionable solutions for future power system planning and operation. Received: 20th October 2025 Revised: 12th November 2025 Accepted: 13th November 2025 Keywords: Renewable energy Voltage stability Optimal dispatch Congestion mitigation Power loss reduction © 2025 Faculty of Engineering, University of Maiduguri, Nigeria. All rights reserved. 1.0 Introduction Traditionally, electricity generated through conventional sources have long proven as most stable but they contributed significantly to global warming as a result of pronounced carbon dioxide (CO2) footprint and capitally expensive in terms of maintenance (Das et al., 2021; Das and De, 2023; Mayer et al., 2020). However, many research effort have been devoted to mitigation of climate change for planet protection (Fawzy et al., 2020; Kaack et al., 2022; Lehmann et al., 2021; Olabi and Abdelkareem, 2022). Subsequently, with the emphasis on mitigating climatic factors, the addition of green energy plants and energy storage (ES) facilities are also important (Monforti-Ferrario and Blanco, 2021; and Sifakis et al., 2021). A search through literatures show wide sparsity of understanding on the related inclusion of renewable energy sources (RESs) and storage units in system stability models (Ahmed et al., 2022; Fatin Ishraque et al., 2021; Miracle et al., 2023; and Numan et al., 2023). 1.1 Background and Context The global migration to sustainable energy has increased the penetration of RESs within electrical distribution systems. While this transformation supports sustainable goals, it as well mitigates dependence on conventional fossil-based energy (Husin et al., 2021; Infield and Freris, 2020; Shafiul Alam et al., 2020; and Sinsel et al., 2020). AZOJETE December 2025. Vol.21(4):1039-1053 Published by the Faculty of Engineering, University of Maiduguri, Maiduguri, Nigeria. Print ISSN: 1596-2490, Electronic ISSN: 2545-5818 https://doi.org/10.63958/AZOJETE/2025/21/04/013 www.azojete.com.ng mailto:zainabobaone@gmail.com mailto:zainabobaone@gmail.com http://www.azojete.com.ng/ Arid Zone Journal of Engineering, Technology and Environment, December 2025; Vol. 21(4): 1039-1053. ISSN 1596-2490; e-ISSN2545-5818; www.azojete.com.ng Corresponding author’s email address: zainabobaone@gmail.com 1040 This transition brings operational challenges because of the intermittent and non-linear structure of RESs (Bajaj and Singh, 2020; Mararakanye and Bekker, 2019; and Worighi et al., 2019). Power losses (PL), non- optimal generation dispatch, voltage instability and congestion are part of the most common pressing issues. The RESs variability and reliance on inverter-based interfaces result to limited reactive power (Q) support and inertial, making systems more prone to voltage instability, especially in weakly interconnected areas. Renewables are often sited far from load centers, requiring long-distance transmission that increases the line losses, while intermittent injections and reverse flows in distribution networks further increase technical losses. In addition, the spatial mismatch between renewable generation hubs and demand centers frequently overloads transmission environments, creating congestion that is harder to manage with reduced availability of flexible classical generation. Although, several studies such as those by Hosseinzadeh et al. (2021), Sifakis et al. (2021), and Yan et al. (2024) have addressed individual objectives like eco-economic optimization and loss minimization, none provide a holistic ORPD framework that rigorously integrates voltage stability constraints together with loss and congestion management. Therefore, there is need to address this gap to mitigate congestion and power losses simultaneously. 2. Methodology Figure 1 presents the systematic review process adopted in this study to analyze recent developments in ORPD frameworks between 2018 and 2024. The flowchart illustrates a structured and transparent review pipeline that ensures reproductivity and minimizes selection bias. The methodology combines elements of scoping review, bibliometric mapping, and comparative thematic analysis, providing both breadth and depth in the investigation of ORPD research. Overall, the figure shows a robust and thorough methodology that combines number-crunching both bibliometric with in-depth qualitative content evaluation, ensuring the results accurately reflect preferred method, current trends and emerging needs within ORPD research. This methodological rigor forms the analytical foundation for the subsequent comparative assessment in Table 1. Figure 1: Methodological Technique for the reviewed studies 2.1 Search Strategy It is evident in Figure 1 that the procedure starts with scoping and planning that involves defining the review boundaries, identifying research gaps, and formulating key search terms around core ORPD parameters, which are, Voltage Stability (VS), Congestion Management (CG) and Power Loss reduction (PL). The search process leveraged multiple reputable databases that include ResearchGate, IEEE Xplore, Google Scholar, and Science Direct, which ensure broad coverage of peer-reviewed publications. 2.2 Inclusion/Exclusion Criteria A total of 1,240 studies were initially retrieved, after which titles and abstracts were screened to exclude non- English papers, duplicates, and those unrelated to ORPD, leaving 350 papers. Further eligibility assessment Optimal Renewable Power Dispatch Review Procedure 1. Scoping 2. Planning 3. Search Process 4. Screening Journals 5. Eligibility Assessment 6. Presentation Interpretation Findings i. Keywords definition: Optimal Renewable Power Dispatch, Renewable Energy, Voltage stability, Congestion and Power Loss ii. Source: Google Scholar, ResearchGate, Science direct, IEEE-explore iii. Got 1240 papers returned from search iv. Screened: Removed title not related to ORPD, and non-English text (350) v. Eligibility: 265 eliminated due to webpage and duplicates vi. Total Studies compared based on streamlined date (2018-2024) are 61 http://www.azojete.com.ng/ mailto:zainabobaone@gmail.com Arid Zone Journal of Engineering, Technology and Environment, December 2025; Vol. 21(4): 1039-1053. ISSN 1596-2490; e-ISSN2545-5818; www.azojete.com.ng Corresponding author’s email address: zainabobaone@gmail.com 1041 filtered out 265 additional papers that lacked relevance datasets, culminating in a final dataset of 61 studies meeting all inclusion criteria. This rigorous filtration underscores the methodological reliability of the study. 2.3 Data Extraction and Classification Figure 1 also captures the data extraction and classification stage, where selected papers were categorized by optimization objective (single or multi-objective), methodological approach (mathematical, metaheuristic, hybrid), and addressed parameters VS, Carbon Footprint (CO2), Active Power (P), Reactive Power (Q), PL, CG, and Energy Storage (ES). The final presentation and interpretation stage involved thematic synthesis, statistical analysis, and visualization through figures, tables and trend charts. 3. ORPD Publications Analysis 3.1 Overview of Reviewed Studies Earlier studies have explored various optimization techniques for RESs dispatch, among which are Genetic Algorithm (Mayer, et al., 2020; and Marouani, et al., 2022), Particle Swarm Optimization (Barakat et al., 2020; and Sharma et al., 2023) and hybrid metaheuristics (Waleed et al., 2022). However, only a limited number explicitly incorporate voltage stability constraints without inclusion of PL and CG objectives (Hosseinzadeh et al., 2021). For example, Ghada et al. (2024) applied Jellyfish Search Algorithm (JSA) to enhance voltage stability using Static Var Compensators. Similarly, Karthik et al. (2021) proposed Levy Interior Search Algorithm (LISA) for reactive power management, showing enhancement of system voltage levels and decreased system losses but neglect congestion framework. African Vulture Optimization Algorithm (AVOA) has also demonstrated effectiveness in large-scale dispatch scenarios in another work (Ali et al., 2023). Numerous studies have also worked on purely economic dispatch of renewable-rich grid systems (Ahmed et al. 2022; Chen et al., 2023; and Yang et al., 2023) and their results showed high performances but neglect of voltage stability indices as a constraint still remain the bottleneck. In other studies, techno-economic dominated their works (Ebeed et al., 2020; Garrido-Arévalo et al., 2024; Sharma et al., 2023; and Xiong et al., 2022) and the methods strength lie in its effectiveness of handling power dispatch issues. Subsequently, some studies delved into planning assessment (Elkadeem et al., 2020; Montoya, 2023; and Shafiul Alam et al., 2020). Furthermore, only a few dogged-on voltage constraints during optimal dispatch (Hosseinzadeh et al., 2021; Abhishek et al., 2023; and Hassan et al., 2020). However, majority of the methods still solved a subset of the problem while the holistic framework is missing. Despite these advances, most studies focus on economic dispatch or emission reduction, often neglecting voltage stability and congestion mitigation (Sifakis et al., 2021; and Purlu and Turkay, 2022). 3.1.1 Voltage stability in electrical energy systems Voltage robustness is crucial in sustaining the power systems’ integrity over centuries especially under high- RES integration. Meanwhile, voltage collapse can result from load demand, imbalance reactive power support and the intermittent nature of RESs (Malbasa et al., 2017; and Pinzón and Colomé, 2019). Also, in the earlier stage, traditional methods such as PV and QV curve analysis enhanced knowledge into static stability margins (Ajjarapu and Christy, 2002), while progressive approaches later provided more authentic assessment of system response to disturbances (Zhang et al., 2015). Subsequently, convectional dispatch models commonly focus on emission or economic objectives, neglecting voltage constraints (Chi and Xu, 2020; and Lin et al., 2022). With present-day grids undergoing progressive complexity, priority has been given to embedded voltage stability indices into optimization techniques. For example, a work proposes an optimal power flow formulation incorporating voltage stability constraints to mitigate the variability of distributed renewable generation (Hosseinzadeh et al., 2021) and another study improved on this method with rigorous formulations that explicitly incorporate renewable uncertainties (Sharma et al., 2023). Regardless of these advances, most studies only focused on stability constraints alone, without combining them to objectives such as congestion mitigation and power loss reduction. 3.1.2 Congestion mitigation in power networks Congestion occurs as a result of unbalance between power supply and power demand due to transmission line exceeding its thermal limit leading to increased losses and reduced reliability (Monforti-Ferrario and Blanco, 2021; Paul et al., 2021; and Tomar, 2024). System congestion has been extensively studied due to its operational and economic significance, especially in deregulation markets. Initial investigations focused on market-based mechanisms namely congestion pricing and locational marginal pricing (Bai et al., 2017), whereas non-market approaches have relied on demand side management, FACTS development and re-dispatch http://www.azojete.com.ng/ mailto:zainabobaone@gmail.com Arid Zone Journal of Engineering, Technology and Environment, December 2025; Vol. 21(4): 1039-1053. ISSN 1596-2490; e-ISSN2545-5818; www.azojete.com.ng Corresponding author’s email address: zainabobaone@gmail.com 1042 (Narain et al., 2020; and Yousefi et al., 2012). Considering the rising penetration of RE, congestion management has exhibited greater complexity arising from variability in transmission and generation limitations (Dashtdar et al., 2020; Paul et al., 2021; Sarwar et al., 2022; Tomar, 2024; and Wang et al., 2018). Even though these techniques deliver effective short-term alleviation, most techniques handle congestion in isolation without explicitly integrating power loss reduction or voltage stability as co-objectives. 3.1.3 Optimal power dispatch An effective power dispatch network is important for sustaining network reliability and enhancing steady system operation, optimizing load demand and reduction of line losses. The economic dispatch issue has classically concentrated on generation costs as well as meeting demand under deterministic conditions (Aziz et al., 2021; Candra et al., 2024; Liu et al., 2021; and Xu et al., 2021). Yet, the stochastic nature of renewable sources query these convectional techniques, propelling research toward robust and stochastic optimization methods (Chi and Xu, 2020; Huang et al., 2019; Lin et al., 2022; Roustaei et al., 2022; and Wang et al., 2020). Recently, hybrid RE to innovative grid-based networks, embedding energy storage mechanisms with demand response have been presented to optimize dispatch flexibility (Liu and Li, 2016), and more current formulations extensively cater for renewable uncertainties during continuous operations (Ding et al., 2020; and Zhang et al., 2020). Despite these formulations enhancing reliability, majority still focus on cost minimization as the primary objective with little attention to congestion or voltage stability constraints. 3.1.4 Power loss reduction methods Improving efficiency through loss minimization in transmission and distribution networks persists as vital operational priority, particularly since the incorporation of RE changes conventional system flow configurations. Traditional methods entail grid restructuring and reactive power compensation (Nkan et al., 2021), though FACTS devices have been evidenced to substantially strengthen system flexibility and decrease power losses (do Carmo Mendonça et al., 2020; Ghada et al., 2024; Rao et al., 2024; Reddy, 2021; and Zubidi et al., 2023). Metaheuristic-based optimization methods encompassing GA, PSO as well as Evolutionary Algorithms (EO) have been excellent approaches for optimizing power loss mitigation techniques in distribution networks (Aljebreen et al., 2023; Karthik et al., 2021; Othman et al., 2025; Sadiq and Antar, 2024; Sambaiah and Jayabarathi, 2020; and Ushashree and Kumar, 2023). Current research trends expand these techniques to renewable-intensive settings, under conditions where uncertainty hinders operational efficiency enhancement (Ali et al., 2023; Eladl and ElDesouky, 2019; Elkadeem et al., 2019; Hmingthanmawia et al., 2023; Khan et al., 2020; and Waleed et al., 2022). Conversely, reducing losses is frequently handled as an independent optimization objective without deliberate alignment with congestion management or stability constraints. 3.1.5 Integrated approaches Addressing the interactions among power system objectives, studies have proposed comprehensive frameworks that account for combination of congestion, stability and losses. Multi-criteria optimization strategies such as algorithms have been broadly adopted to achieve equilibrium among divergent objectives (Infield and Freris, 2020; Ismael et al., 2019; Mohandes et al., 2019; and Sinsel et al., 2020). Some studies have shown high system performance with embedded stability and congestion objectives in OPF frameworks. (Sekhavatmanesh and Cherkaoui, 2018;, and Zhao et al., 2022). Alternatively, other studies have merged FACTS placement with multi-objective optimization for grid performance efficiency (Li, 2024). Nevertheless, these researches typically examine bi-objective configurations instead of unified models. Therefore, existing study considering voltage stability-constraints, RE dispatch, congestion mitigation and power loss reduction within an inclusive optimization domain remains limited. 3.2 Emerging Themes Table 1 provides a detailed comparison of 61 studies on ORPD published between 2018 and 2024. It captures key attributes including optimization orientation, quadratic function usage, addressed parameters, and applied methods. The table serves as the foundation of progress identification, research gaps and thematic synthesis. Therefore, it is explicitly explained using figures 1 to 5. http://www.azojete.com.ng/ mailto:zainabobaone@gmail.com Arid Zone Journal of Engineering, Technology and Environment, December 2025; Vol. 21(4): 1039-1053. ISSN 1596-2490; e-ISSN2545-5818; www.azojete.com.ng Corresponding author’s email address: zainabobaone@gmail.com 1043 Table 1: Comparative analysis of studies on ORPD from 2018-2024 S/N Author with Date MO/ SO QD Problem Addressed Method used VS CO2 P Q PL CG ES 1 Kholi and Arora (2018) MO ☑ ☑ ☑ ☑ ☑ ☑ ⮽ ⮽ CGWO 2 Biswas et al., (2018) MO ☑ ☑ ☑ ☑ ☑ ☑ ⮽ ⮽ DEA 3 Biswas et al., (2018) both ☑ ⮽ ⮽ ☑ ☑ ☑ ⮽ ⮽ SHADE 4 Suresh et al., (2019) MO ☑ ☑ ☑ ☑ ⮽ ☑ ⮽ ⮽ ADFA 5 Elattar et al., (2019 SO ☑ ☑ ☑ ☑ ☑ ☑ ☑ ⮽ MJAYA 6 Eladi et al., (2019) SO ☑ ⮽ ⮽ ☑ ⮽ ⮽ ⮽ ⮽ PSO-SOP 7 Velasquez et al., (2019) SO ☑ ⮽ ⮽ ☑ ⮽ ⮽ ⮽ ⮽ DMPC 8 Chen et al., (2019) MO ☑ ☑ ☑ ☑ ☑ ⮽ ☑ ⮽ CMOPEO+SF 9 Naval et al., (2019) SO ⮽ ⮽ ⮽ ☑ ⮽ ⮽ ⮽ ⮽ MILP 10 Alomoush, (2019) both ☑ ⮽ ☑ ☑ ⮽ ☑ ⮽ ⮽ SFS 11 Elkadeem et al., (2019) MO ☑ ☑ ⮽ ☑ ⮽ ☑ ☑ ⮽ HHO-PSO 12 Wang et al., (2019) SO ⮽ ⮽ ⮽ ⮽ ☑ ☑ ⮽ ⮽ PSO 13 Vaziri et al., (2019) MO ⮽ ⮽ ⮽ ⮽ ⮽ ⮽ ⮽ ⮽ MILP 14 Zhou et al., (2019) SO ☑ ☑ ⮽ ☑ ⮽ ⮽ ⮽ ☑ ATC 15 Hooshmand et al., (2019) MO ☑ ☑ ⮽ ☑ ☑ ☑ ⮽ ☑ MISOCP 16 Wang et al., (2020) SO ⮽ ⮽ ⮽ ☑ ⮽ ⮽ ⮽ ☑ IBESSD 17 Hassan et al., (2020) MO ☑ ☑ ☑ ☑ ☑ ☑ ☑ ☑ MNLP 18 Inam et al., (2020) SO ☑ ☑ ☑ ☑ ☑ ☑ ⮽ ⮽ GWO 19 Nusair et al., (2020) both ☑ ☑ ☑ ☑ ☑ ☑ ⮽ ⮽ GROM 20 Ebeed et al., (2020) MO ☑ ☑ ⮽ ☑ ☑ ☑ ⮽ ⮽ MPA 21 Murty and Kumar, (2020) MO ☑ ⮽ ☑ ☑ ⮽ ⮽ ⮽ ☑ HOMER 22 Abazal et al., (2020) both ☑ ☑ ⮽ ☑ ☑ ☑ ⮽ ⮽ ECOA 23 Ebeed et al., (2020) MO ☑ ☑ ⮽ ☑ ☑ ☑ ⮽ ⮽ ILAPO 24 Yousif et al., (2020) MO ☑ ⮽ ☑ ☑ ⮽ ☑ ⮽ ☑ PSO 25 Chamandous et al., (2020) MO ☑ ⮽ ☑ ☑ ⮽ ⮽ ⮽ ☑ GAMS/DICOPT 26 Elattar and Elsayed (2020) SO ☑ ☑ ☑ ☑ ☑ ☑ ☑ ⮽ MMFO 27 Ettappan et al., (2020) MO ☑ ☑ ⮽ ☑ ☑ ☑ ⮽ ⮽ ABC 28 Gbadamosi and Nwulu (2020) MO ☑ ⮽ ☑ ☑ ⮽ ☑ ⮽ ⮽ MIQP 29 Xu et al., (2020) MO ⮽ ⮽ ☑ ☑ ⮽ ⮽ ⮽ ☑ PSO 30 Vishnu et al., (2020) MO ☑ ☑ ⮽ ☑ ☑ ☑ ⮽ ⮽ DEPSO 31 Azralmukmin et al., (2020) SO ☑ ⮽ ⮽ ☑ ⮽ ☑ ⮽ ⮽ COR 32 Hodge et al., (2020) MO ☑ ☑ ⮽ ☑ ⮽ ☑ ⮽ ⮽ Systematic 33 Ismail et al., (2020) MO NS ☑ ⮽ ☑ ☑ ☑ ☑ ☑ Systematic 34 Nikkhah et al., (2020) SO ☑ ☑ ⮽ ☑ ☑ ☑ ⮽ ☑ MINLP 35 Nasr et al., (2020) MO ☑ ☑ ⮽ ☑ ☑ ☑ ⮽ ☑ MINLP 36 Ishraque et al., (2021) MO ☑ ☑ ☑ ☑ ⮽ ☑ ⮽ ☑ HOMER 37 Liu et al., (2021) MO ☑ ⮽ ⮽ ☑ ⮽ ⮽ ⮽ ☑ NSGAII+TOPSI S 38 Sakthivel et al., (2021) MO ☑ ⮽ ☑ ☑ ⮽ ☑ ☑ ⮽ MOSSA 39 Ogunmodede et al., (2021) SO ⮽ ⮽ ⮽ ☑ ⮽ ⮽ ⮽ ☑ MILP 40 Liu et al., (2021) MO ⮽ ☑ ☑ ☑ ⮽ ☑ ⮽ ☑ IOD 41 Bravo et al., (2021) MO ☑ ⮽ ☑ ☑ ⮽ ⮽ ⮽ ☑ MOO 42 Ordoudis et al., (2021) MO ☑ ⮽ ☑ ☑ ⮽ ⮽ ☑ ☑ DCC 43 Guo et al., (2021) MO ⮽ ⮽ ☑ ⮽ ⮽ ⮽ ⮽ ☑ Bi-Level 44 Sifakis et al., (2021) MO ☑ ⮽ ☑ ☑ ⮽ ⮽ ⮽ ☑ HOMER 45 Liu et al., (2021) MO ☑ ⮽ ☑ ☑ ⮽ ☑ ⮽ ⮽ MFO-PDU 46 Hassan et al., (2021) both ☑ ☑ ⮽ ☑ ☑ ☑ ⮽ ☑ Systematic 47 Xie et al., (2021) SO ⮽ ☑ ⮽ ☑ ☑ ☑ ☑ ⮽ Not specified http://www.azojete.com.ng/ mailto:zainabobaone@gmail.com Arid Zone Journal of Engineering, Technology and Environment, December 2025; Vol. 21(4): 1039-1053. ISSN 1596-2490; e-ISSN2545-5818; www.azojete.com.ng Corresponding author’s email address: zainabobaone@gmail.com 1044 Table 1 contd.: Comparative analysis of studies on ORPD from 2018-2024 S/N Author with Date MO/ SO QD Problem Addressed Method used VS CO2 P Q PL CG ES 48 Mohandes et al., (2021) MO ⮽ ⮽ ⮽ ☑ ⮽ ⮽ ⮽ ☑ GA 49 Saddique et al., (2022) SO ☑ ☑ ⮽ ⮽ ☑ ☑ ⮽ ⮽ CA 50 Vergara et al., (2022) SO ☑ ☑ ⮽ ☑ ☑ ☑ ⮽ ⮽ RL 51 Liu et al., (2022) MO ☑ ☑ ⮽ ⮽ ☑ ☑ ⮽ ⮽ IGA 52 Purlu et al., (2022) both ☑ ☑ ⮽ ☑ ☑ ☑ ⮽ ☑ GA, PSO 53 Ali et al., (2023) MO ☑ ☑ ⮽ ☑ ☑ ☑ ⮽ ☑ Monte Carlo 54 Gholami et al., (2023) both ☑ ⮽ ☑ ☑ ⮽ ☑ ⮽ ⮽ SACDE 55 Chapaloglou et al., (2023) MO ☑ ☑ ☑ ☑ ⮽ ☑ ⮽ ☑ Probabilistic 56 Chen et al., (2023) SO ⮽ ⮽ ⮽ ☑ ⮽ ☑ ⮽ ⮽ Two-Stage 57 Miracle et al., (2023) MO ☑ ⮽ ⮽ ☑ ⮽ ☑ ⮽ ⮽ POCSO 58 Barnawi et al., (2024) MO NS ☑ ☑ ☑ ☑ ☑ ⮽ ☑ GNDO 59 Li et al., (2024) MO ☑ ☑ ⮽ ☑ ☑ ☑ ⮽ ⮽ PSO 60 Mohammed et al., (2024) both ☑ ☑ ☑ ☑ ☑ ☑ ☑ ⮽ CGO 61 Alayande et al., (2024) MO ☑ ☑ ☑ ☑ ☑ ☑ ⮽ ☑ BBS ABC: Artificial Bee Colony; ADFA: Ameliorated Dragonfly Algorithm; ATC: Analytical Target Cascading; BBS: Bus bar Synchronization; CGO: Chaos Game Optimization; CGWO: Constrained Grey Wolf Optimization; CMOPEO+SF: Constrained Multi-objective Population Extrema Optimization; DEA: Differential Evolution Algorithm; DEPSO: Diversity Enhanced PSO; DMCP: Distributed Model Predictive Control; ECOA: Enhanced Coyote Optimization Algorithm; GA: Genetic Algorithm; GROM: Golden Ratio Optimization Method; GWO: Grey Wolf Optimization; HHO: Harris Hawks Optimization; HOMER: Hybrid Optimization of Multiple Energy Resources; IBESSD: Improved Battery Energy Storage; IGA: Improved Genetic Algorithm; ILAPO: Improved Lightening Attachment Procedure Optimization; IOD: Integrated Optimal Dispatch Strategy; MFO-PDU: Enhanced Moth Flame Optimization; MILP: Mixed Integral Linear Programming MINLP: Mixed Integral Non-Linear Programming; MIQP: Mixed Integer Quadratic Programming; MISOCP: Mixed Integral with Soft Computing Programming; MMFO: Modified Month Flame Optimization; MO: Multi-Objective; MOSA: Modified Optimization Swarm Algorithm; MPA: Marine Predator Algorithm; NS: Not specified; PSO: Particle Swarm Optimization; QD: Quadratic Function; RL: Reinforcement Learning; SACDE: Self-Adaptive Comprehensive Differential Evolution Algorithm; SCA: Sine-Cosine Algorithm; SFS: Stochastic Fractal Search; SHADE: Success History Based Adaptive Differential Evolution; SO: Single Objective; SQP: Sequential Quadratic Programming 3.2.1 ORPD publications Figure 2 maps the yearly count of reviewed studies on ORPD between 2018 and 2024. The publication history shows research interest in the field has fluctuated but grown overall. The lowest publication numbers were recorded in 2018 and 2024, with three studies each. This suggests the field was either in its early exploratory stages (2018) or potentially reaching a point of saturation or shifting to newer research paradigms (2024). The peak was reached in 2020, with 16 published studies. This surge likely reflects increased global efforts to better integrate renewables, alongside the practical maturity of metaheuristic optimization tools (Das et al., 2021; and Elkadeem et al., 2020). The high output that year could also be tied to advancements in hybrid optimization algorithms and the development of smart grid integration models (Panda & Nayak, 2022; and Liu et al., 2021). The steady, moderate number of studies between 2019 and 2021 (12 each year) suggests the research consolidated into practical topics like reactive power dispatch, stability analysis, and congestion management (Hosseinzadeh et al., 2021; and Sharma et al., 2023). However, the drop-off after 2022 (down to four or five studies) points to a subtle shift in focus. While the core field has matured, attention is gradually moving toward AI-based, decentralized, and data-driven dispatch models (Chen et al., 2023; and Ali et al., 2023). Summarily, Figure 2 shows that ORPD research reached its height during the peak period of global renewable integration, and the current, evolving interest is centered on developing advanced hybrid and intelligent optimization models for a more resilient and sustainable grid. 3.2.2 Objective function types A clear pattern emerged from the tabulated data resulted to figure 3 where multi-objective (MO) optimization frameworks overwhelmingly dominate resent research, accounting for more than half of the reviewed works (63.90%). These approaches simultaneously target multiple goals such as emission reduction, power loss mitigation, cost minimization, and voltage stability enhancement. Conversely, single-objective (SO) http://www.azojete.com.ng/ mailto:zainabobaone@gmail.com Arid Zone Journal of Engineering, Technology and Environment, December 2025; Vol. 21(4): 1039-1053. ISSN 1596-2490; e-ISSN2545-5818; www.azojete.com.ng Corresponding author’s email address: zainabobaone@gmail.com 1045 formulations constitute 23.00%, though still relevant and once common in early economic dispatch studies, but gradually declining due to their inability to handle multidimensional nature renewable-dominated grids. A small subset of studies (13.10%) employed hybrid structures, reflecting the transition towards adaptive and flexible optimization paradigms. Figure 2: ORPD Publication Analysis Figure 3: Objective Function Types 3.2.3 Methodology trends Regarding optimization algorithms, figure 4 highlights the rapid development of computational intelligence in ORPD. Widely used metaheuristic algorithms (57%) include PSO, GA, GWO, HHO, and ABC, all valued for their ability to manage complex, non-linear dispatch problems. Hybrid and advanced methods like PSO–SQP, HHO–PSO, MPA and ILAPO signal a methodological drive (12%) toward better convergence reliability and more accurate solutions. Recent studies also incorporate AI-based frameworks (5%) such as Reinforcement Learning (RL), Self-Adaptive Comprehensive Differential Evolution (SACDE), and Chaos Game Optimization (CGO), underlining the ongoing digital transformation of power-system optimization. 3.2.4 Multi-parameter synergy Looking at the seven parameters addressed, P and PL optimization dominate the research landscape, featuring in over two-thirds of the reviewed papers. VS shows as a constraint or goal in a fair number of studies, underscoring its rising importance especially with more renewables, it is still underrepresented relative to purely economic objectives. CG and ES are noticeably underexplored, appearing in fewer than 20% of the works. This evidence confirms a major gap in fully integrated dispatch models. Very view studies specifically http://www.azojete.com.ng/ mailto:zainabobaone@gmail.com Arid Zone Journal of Engineering, Technology and Environment, December 2025; Vol. 21(4): 1039-1053. ISSN 1596-2490; e-ISSN2545-5818; www.azojete.com.ng Corresponding author’s email address: zainabobaone@gmail.com 1046 those using MILP or Bi-Level techniques explicitly bring CG stability and loss into a single framework. Moreover, Figure 5 highlights the growing inclusion of Battery Energy Storage System (BESS) and Hybrid RE Systems (HRES) in dispatch formulations, confirming a welcome shift towards designing for flexibility and resilience, and better dynamic balancing into grid. The recent (2022-2024) to advanced AI techniques like RL, SACDE, and CGO shows an emerging trend towards AI-driven. Data-adaptive optimization models. Hence, the transition underscores just how complex power system operation has become in grid dominated by renewables. Figure 4: Optimization Methods Figure 5: Multi-Parameter Variables 3.3 Implications for Future Research and Practice The analysis of the tables and figures provides several critical implications for the future of ORPD: The dominance of multi-objective optimization highlights that dispatch in renewable-integrated grids is inherently multi-dimensional. This confirms that simple single-objective models, which only focus on minimizing cost or loss, are inadequate because they ignore crucial factors like voltage stability and reliability. The shift in algorithms from classical methods to hybrid and AI-based models signals a move toward intelligent and adaptive dispatch. Methods like RL, Genetic–Swarm hybrids, and Self-Adaptive Differential Evolution manage system non-linearity and uncertainty better and will likely become the standard as grid complexity increases. http://www.azojete.com.ng/ mailto:zainabobaone@gmail.com Arid Zone Journal of Engineering, Technology and Environment, December 2025; Vol. 21(4): 1039-1053. ISSN 1596-2490; e-ISSN2545-5818; www.azojete.com.ng Corresponding author’s email address: zainabobaone@gmail.com 1047 The parameter synergy analysis (Figure 5) confirms that while active power and loss minimization are mature fields, parameters like voltage stability, congestion management, and energy-storage integration are underdeveloped. This means current strategies risk operational efficiency without ensuring long-term grid resilience. The priority must be to develop unified frameworks that integrate these critical, underexplored factors. Subsequently, publication trends show that after a period of theoretical development, researchers are now pivoting toward implementation-oriented and application-specific frameworks, particularly in areas like microgrids and peer-to-peer energy markets. Finaly, the future of ORPD lies in multi-parameter, AI-driven, and stability-constrained optimization systems that are essential for supporting flexible, sustainable, and autonomous power networks. 4. Conclusion This review has thoroughly analyzed recent developments in ORPD between 2018 and 2024. The findings confirm that while major strides in optimization modelling, existing frameworks often suffer from fragmentation, treating essential elements like voltage stability, power losses, and congestion as isolated objectives instead of as interconnected operational constraints. The literature reveals a historical bias toward cost-driven dispatch, where economic optimization often overshadows the need for reliability and voltage security. As a result, system operators continue to struggle with maintaining stable voltage profiles, reducing line losses, and managing congestion under high renewable penetration. Though advanced multi-objective and metaheuristic algorithms (like GA, PSO, HHO, and their hybrids) have been successful in balancing cost and loss, they frequently fail to include real-time voltage stability constraints and network congestion parameters. Few integrated models exist that simultaneously tackle all the critical variables: voltage stability, congestion mitigation, and power loss reduction within a single optimization domain. Based on this synthesis, the three top priorities identified for future ORPD research are: 1. Developing unified frameworks that holistically combine voltage stability indices, congestion control, and loss minimization, all while accounting for renewable energy uncertainty. 2. Adopting intelligent, data-driven optimization techniques like machine learning and reinforcement learning to boost the adaptability and decision speed of dispatch systems. 3. Integrating energy storage and smart-grid coordination to enhance system resilience, enable real-time stability control, and optimize distributed renewable generation. To achieve reliable and sustainable renewable-dominated grids, the industry must transit from traditional, cost- based methods to multi-objective, stability-constrained, and AI-supported ORPD frameworks. These advanced systems will not only improve power quality and voltage security but will also dramatically accelerate the shift toward a carbon-neutral and economically efficient energy future. References Alayande, AS., Adeyemi RT., Okakwu, IK., Awosope, CAO., and Omogoye, OS. 2024. State of the Art of Optimal Generation Dispatch in a Renewable Energy-Dominated Power System. SSRN 4716223:12. Ahmed, I., Alvi, UEH., Basit, A., Khursheed, T., Alvi, A., Hong, KS., and Rehan, M. 2022. A novel hybrid soft computing optimization framework for dynamic economic dispatch problem of complex non-convex contiguous constrained machines. PloS One, e0261709: 17(1). Ajjarapu, V., and Christy, C. 2002. The continuation power flow: a tool for steady state voltage stability analysis. IEEE Transactions on Power Systems, 7(1): 416-423. Al-Shetwi, AQ., Hannan, MA., Jern, KP., Mansur, M., and Mahlia, TMI. 2020. Grid-connected renewable energy sources: Review of the recent integration requirements and control methods. Journal of Cleaner Production, 2020. Article ID 119831: 253. Ali, A., Abbas, G., Keerio, MU.,Touti, E., Ahmed, Z., Alsalman, O., Kim, Y., and Member, S. 2023. A Bi-Level Techno-Economic Optimal Reactive Power Dispatch Considering Wind and Solar Power Integration. IEEE Access, 11(June): 62799-62819. http://www.azojete.com.ng/ mailto:zainabobaone@gmail.com Arid Zone Journal of Engineering, Technology and Environment, December 2025; Vol. 21(4): 1039-1053. ISSN 1596-2490; e-ISSN2545-5818; www.azojete.com.ng Corresponding author’s email address: zainabobaone@gmail.com 1048 Aljebreen, KS., Hussein, AE., and Abido, MA. 2023. Optimum Allocation of Distributed Energy Resources for Voltage Stability Enhancement and Loss Reduction. IEEE EUROCON 2023-20th International Conference on Smart Technologies, 227-232. Aziz, T., Lin, Z., Waseem, M., and Liu, S. 2021. Review on optimization methodologies in transmission network reconfiguration of power systems for grid resilience. International Transactions on Electrical Energy Systems, 31(3): e12704. Bachtiar NM., Arief, A., amd Bansal, RC. 2014. Transmission management for congested power system. A review of concepts, technical challenges and development of a new methodology. Renewable and Sustainable Energy Reviews, 38:572-580. Bai, L., Wang, J., Wang, C., Chen, C., and Li, F. 2017. Distribution locational marginal pricing (DLMP) for congestion management and voltage support. IEEE Transactions on Power Systems, 33(4): 4061-4073. Bajaj, M., and Singh, AK. 2020. Grid integrated renewable DG systems: A review of power quality challenges and state-of-the-art mitigation techniques. International Journal of Energy Research, 44(1): 26-69. Barakat, S., Ibrahim, H., and Elbaset, AA. 2020. Multi-objective optimization of grid-connected PV-wind hybrid system considering reliability, cost, and environmental aspects. Sustainable Cities and Society, 60(April): 102178. https://doi.org/10.1016/j.scs.2020.102178 Biswas, PP., Suganthan, PN., Mallipeddi, R., and Amaratunga, GAJ. 2019. Optimal reactive power dispatch with uncertainties in load demand and renewable energy sources adopting scenario-based approach Applied Soft Computing Journal, 75: 616-632. https://doi.org/10.1016/j.asoc.2018.11.042 Candra, O., Alghamdi, MI., Hammid, AT., Alvarez, JRN., Staroverova, OV., Hussein, AA., Marhoon, HA., and Shafieezadeh, MM. 2024. Optimal distribution grid allocation of reactive power with a focus on the particle swarm optimization technique and voltage stability. Science Reports, 14(1): 10889. Chen, W., Han, Y., and Liu, F. 2023. Stability Constrained Two-Stage Robust Optimization Model for Migrogrid Economic Dispatching. Journal of Physics: Conference Series, 2496(1): 12028. Chi, Y., and Xu, Y. 2020. Multi-stage coordinated dynamic VAR source placement for voltage stability enhancement of wind-energy power sysytem. IET Generation, Transmission and Distribution, 14(6): 1104- 1113. Chondrogiannis, S., Poncela-Blanco, M., Marinopoulos, A., Marneris, I., Ntomaris, A., Biskas, P., and Bakirtzis, A. 2021. Power system flexibility: A methodological analytical framework based on unit commitment and economic dispatch modelling. In A. Dagoumas (Ed.), Mathematical Modelling of Contemporary Electricity Markets. Academic Press: Chapter 8:122-156. https://doi.org/10.1016/B978-0-12-821838-9.00008-6 Das, BK., Hassan, R., Tushar, MSHK., Zaman, F.,Hassan, M., and Das, P. 2021. Techno-economic and environmental assessment of a hybrid renewable energy system using multi-objective genetic algorithm: A case study for remote Island in Bangladesh. Energy Conversion and Management, 230:113823. Das, S., and De, S. 2023.Technically efficient, economic and environmentally benign hybrid decentralized energy solution for an Indian village: Multi criteria decision making approach. Journal of Cleaner Production, 388:135717. Dashtdar, M., Najafi, M., and Esmaeilbeig, M. 2020. Calculating the locational marginal price and solving optimal power flow problem based on congestion management using GA-GSF algorithm, Electrical Engineering, 102(3): 1549-1566. Ding, F., Zhang, Y., Simpson, J., Bernstein, A., and Vadari, S. 2020. Optimal energy dispatch of distributed PVs for the next generation of disribution management systems. IEEE Open Access Journal of Power and Energy, 7: 287-295. http://www.azojete.com.ng/ mailto:zainabobaone@gmail.com https://doi.org/10.1016/j.scs.2020.102178 https://doi.org/10.1016/j.asoc.2018.11.042 https://doi.org/10.1016/B978-0-12-821838-9.00008-6 Arid Zone Journal of Engineering, Technology and Environment, December 2025; Vol. 21(4): 1039-1053. ISSN 1596-2490; e-ISSN2545-5818; www.azojete.com.ng Corresponding author’s email address: zainabobaone@gmail.com 1049 Do Carmo Mendonca, D., Cupertino, AF., Pereira, HA., and Teodorescu, R. 2020. Minimum cell operation control for power loss reduction in MMC-based STATCOM. IEEE Journal of Emerging and Seleccted Topics in Power Electronics, 9(2): 1938-1950. Ebeed, M., Ali, A., Mosaad, MI., and Kamel, S. 2020. An improved lightning attachment procedure optimizer for optimal reactive power dispatch with uncertainty in renewable energy resources. IEEE Access, 8:168721- 168731. https://doi.org/10.1109/ACCESS.2020.3022846 Eshan, A., and Yang, Q. 2018. Optimal integration and planning of renewable distributed Generation in the power distribution networks: A review of analytical techniques. Applied Energy, 210:44-59. Eladl, AA., and ElDesouky, AA. 2019. Optimal economic dispatch for multi heat-electric energy source power system. International Journal of Electrical Power and Energy Systems, 110:21-35. https://doi.org/10.1016/j.ijepes.2019.02.040 Elkadeem, MR., Abd Elaziz, M., Ullah, Z., Wang, S., and Sharshir, SW. 2019. Optimal planning of Renewable Energy-Integrated Distribution System Considering Uncertainties. IEEE Access, 7:164887-164907. https://doi.org/10.1109/ACCESS.2019.2947308 Elkadeem, MR., Wang, S., Azmy, AM., Atiya, EG., Ullah, Z., and Sharshir, SW. 2020. A systematic decision- making approach for planning and assessment of hybrid renewable energy-based microgrid with techno- economic optimization. A case study of an urban community in Egypt. Sustainable Cities and Society, 54(April 2019): 102013. https://doi.org/10.1016/j.scs.2019.102013 Fatin Ishraque, M., Shezan, SA., Ali, MM. 2021. Optimization of load dispatch strategies for an Islanded microgrid connected with renewable energy sources. Applied Energy, 292. https://doi.org/10.1016/j.apenergy.2021.116879 Fawzy, S., Osman, AI., Doran, J., and Rooney DW. 2020. Strategies for mitigation of climate change: A review. Environmental Chemistry Letters, 18: 2069-2094. Garrido-Arevalo, VM., Gil-Gonzalez, W., Montoya, OD., Grisales-Norena, LF., and Hernandez, JC. 2024. Optimal Dispatch of DERs and Battery-Based ESS in Distribution Grids while Considering Reactive Power Capabilities and Uncertainties: a Second -Order Cone Programming Formulation. IEEE Access, 12:48497- 48510. Ghada, M., Ahmed, G., Mohammed, A., and Anwar, G. 2024. Jellyfis Search Algorithm-Based Optimal Reactive Power DispatchConsidering SVC Devices for Voltage Stability Enhancement. 2024 2nd International Conference on Electrical Engineering and Automatic Control (ICEEAC), 1-6. Gholami, K., Islam, MR., Rahman, MM., Azizivahed, A., and Fekih, A. 2022. State-of-the-art technologies forvolt- var control to support the penetration of renewable energy into the smart distribution grids. Energy Reports, 8:8630-8651. Gumpu, S., Pamulaparthy, B., and Sharma, A. 2019. Review of congestion management methods from conventional to smart grid scenario. International Journal of Emerging Electric Power Systems, 20(3): 20180265. Gupta, M., Kumar, V., Banerjee, GK., and Sharma, NK. 2017. Mitigating Congetsion in a Power System and Role of FACTS Devices. Advances in Electrical Engineerin, 2017:1-7. https://doi.org/10.1155/2017/4862428 Hamoud, G., and Bradley, I. 2004. Assessment of transmission congestion cost and locational marginal pricing in a competitive electricity market. IEEE Transactions on Power Systems, 19(2):769-775. Hmingthanmawia, D., Deb, S., Datta, S., and Singh, KR. 2023. Multi-Objective based Economic Dispatch and Loss Reduction considering Grasshopper Optimization Algorithm. 2023 9th International Conference on Electrical Energy Systems (ICEES), 585-589. http://www.azojete.com.ng/ mailto:zainabobaone@gmail.com https://doi.org/10.1016/j.ijepes.2019.02.040 https://doi.org/10.1109/ACCESS.2019.2947308 https://doi.org/10.1016/j.scs.2019.102013 https://doi.org/10.1016/j.apenergy.2021.116879 https://doi.org/10.1155/2017/4862428 Arid Zone Journal of Engineering, Technology and Environment, December 2025; Vol. 21(4): 1039-1053. ISSN 1596-2490; e-ISSN2545-5818; www.azojete.com.ng Corresponding author’s email address: zainabobaone@gmail.com 1050 Hosseinzadeh, N., Aziz, A., Mahmud, A., Gargoom, A., and Rabbani, M. 2021. Voltage stability of power systems with renewable-energy inverter-based generators: A review. Electronics, 10(2):15. Huang, B., Liu, L., Zhang, H., Li, Y., and Sun, Q. 2019. Distributed optimal economic dispatch for microgrids considering communication delays. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 49(8): 1634- 1642. Husin, H., and Zaki, M. 2021. A critical review of the integration of renewable energy sources with various technologies. Protection and Control of Modern Power Systems, 6(1):1-18. Infield, D., and Freris, L. 2020. Renewable energy in power systems. John Willey and Sons. Ismael, SM., Aleem, SHEA., Abdelaziz, AY., and Zobaa, AF. 2019. State-of-the-art of hosting capacity in modern power systems with distributed generation. Renewable Energy, 130: 1002-1020. Kaack, LH., Donti, PL., Strubell, E., Kamiya, G., GReutzig, F., and Rolnick, D. 2022. Aligning artificial intelligence with climate change mitigation. Nature Climate Change, 12(6): 518-527. Karthik, N., Parvathy, AK., Arul, R., and Padmanathan, K. 2021. Multi-objective optimal power flow using a new heuristic optimization algorithm with the incorporation of renewable energy sources. International Journal of Energy and Environmental Engineering, 12(4): 641-678. Khan, IU., Javaid, N., Gamage, KAA., James Taylor, C., Baig, S., and Ma, X. 2020. Heuristic Algorithm Based Optimal Power Flow Model Incorporating Stochastic Renewable Energy Sources. IEEE Access, 8:148622- 148643. https://doi.org/10.1109/ACCESS.2020.3015473 Kim, SC., and Salkut, SR. 2019. Optimal power flow based congestion management using enhanced genetic algorithms. International Journal of Electrical and Computer Engineering, 9(2): 2088-8708. Kumar, A., Deng, Y., He, X., Singh, AR., Kumar, P., Bansal, RC., Bettayeb, M., Ghenai, C., and Naidoo, RM. 2023. Impact of demand side management approaches for the enhancement of voltage stability loadability and customer satisfaction index. Applied Energy, 339:120949. Kumar, A., and Sekhar, C. DSM based congestion management in pool markets with FACTS devices. Energy Procedia, 14:94-100. Kundur, P. 2007. Power system. Power System Stability and Control, 10(1): 1-7. Lehmann, J., Cowie, A., Masiello, CA., Kammann, C., Woolf, D., Amonette, JE., Cayuela, ML., Camps-Arbestain, M., and Whitman, T. 2021. Biochar in climate change mitigation. Nature Geoscience, 14(12):883-892. Li, Y. 2024. Advanced intelligent optimization algorithms for multi-objective optimal power flow in future power systems: a review. ArXiv Preprint ArXiv:2404.09203. Lin, Y., Zhang, X., Wang, J., Shi, D., and Bian, D. 2022. Voltage stability constrained optimal power flow for unbalanced distribution system based on semidefinite programming. Journal of Modern Power Systems and Clean Energy, 10(6): 1614-1624. Liu, J., and Li, J. 2016. Interractive energy-saving dispatch considering generation and demand Side uncertainties: a Chinese study. IEEE Transactions on Smart Grid, 9(4): 2943-2953. Liu, Z., Xiao, Z., Wu, Y., Hou, HUI., and Xu, TAO. 2021. Integrated Optimal Dispatching Strategy Considering Power Generation and Consumption Interaction. 9.https://doi.org/10.1109/ACCESS.2020.3045151 Lu, X., Li, H., Zhou, K., and Yang, S. 2023. Optimal load dispatch of energy hub considering uncertainties of renewable energy and demand response. Energy, 262:125564. Malbasa, V., Zheng, C., Chen, PC., Popovic, T., and Kezunovic, M. 2017. Voltage stability prediction using active machine learning. IEEE Transactions on Smart Grids, 8(6):3117-3124. http://www.azojete.com.ng/ mailto:zainabobaone@gmail.com https://doi.org/10.1109/ACCESS.2020.3015473 https://doi.org/10.1109/ACCESS.2020.3045151 Arid Zone Journal of Engineering, Technology and Environment, December 2025; Vol. 21(4): 1039-1053. ISSN 1596-2490; e-ISSN2545-5818; www.azojete.com.ng Corresponding author’s email address: zainabobaone@gmail.com 1051 Mararakanye, N., and Bekker, B. 2019. Renewable energy integration impacts within the context of generator type, penetration level and grid characteristics. Renewable and Sustainable Energy Reviews, 108(October 2018):441-451. https//doi.org/10.1016/j.ser.2019.03.045 Marouani, I., Guesmi, T., Abdallah, HH., Alshammari, BM., Alqunun, K., Alshammari, AS., and Rahmani, S. 2022. Combined Economic Emission Dispatch with and without Consideration of PV and Wind Energy by using Various Optimization Techniques: A Review. Energies, 15(12). Mayer, MJ.,Szilagyi, A., and Grof, G. 2020. Environmental and economic multi-objective optimization of a household level hybrid renewable energy system by genetic algorithm. Applied Energy, 269(April): 115058. https://doi.org/10.1016/j.apenergy.2020.115058 Miracle, DB., Viral, RK., Tiwari, PM., and Bansal, M. 2023. Hybrid Metaheuristic Model for Optimal Economic Load Dispatch in Reenewable Hybrid Energy System. International Transactions on Electrical Energy Systems, 2023:10-12. https://doi.org/10.1155/2023/5395658 Mohandes, B., El Moursi, MS., Hatziargyriou, N., and El Khatib, S. 2019. A review of power system flexibility with high penetration of renewables. IEEE Transactions on Power Systems, 34(4):3140-3155. Monforti-Ferrario, F., and Blanco, MP. 2021.the impact of power network congestion, its consequences and mitigation measures on air pollutants and greenhouse gases emmissions. A case from Germany. Renewable and Sustainable Energy Reviews, 150:111501. Montoya, OD., Florez-Cediel, OD., and Gil-Gonzalez, W. 2023. Efficient day-ahead sceduling of pv-statcoms in medium-voltage distribution networks using a second-order cone relaxation. Computers, 12(7): 142. Narain, A., Srivastava, SK., and Singh, SN. 2020. Congestion management approaches in restructure power sysytem: Key issues and challenges. The Electricity Journal, 33(3): 106715. Naval, N., Sánchez, R., and Yusta, JM. 2020. A virtual power plant optimal dispatch model with large and small- scale distributed renewable generation. Renewable Energy, 151: 57–69. https://doi.org/10.1016/j.renene.2019.10.144 Nkan, IE., Okpo, EE., and Okoro, OI. 2021. Multi-type FACTS controllers for power system compensation: A case study of the Nigerian 48-bus, 330 kV system. Nigerian Journal of Technological Development, 18(1): 63– 69. Numan, M., Baig, MF., and Yousif, M. 2023. Reliability evaluation of energy storage systems combined with other grid flexibility options: A review. Journal of Energy Storage, 63: 107022. Olabi, AG., and Abdelkareem, MA. 2022. Renewable energy and climate change. Renewable and Sustainable Energy Reviews, 158: 112111. Othman, AF., Othman, ML., Ab Kadir, MZA., Izzri, N., Wahab, A., and Abidin, AAZ. 2025. Optimizing Voltage Profile and Mitigating Power Losses in Distribution Network Reconfiguration Via the Integration of Distributed Generation Penetration in Malaysia. Network, 133(1): 120–134. Paliwal, P. 2021. Comprehensive analysis of distributed energy resource penetration and placement using probabilistic framework. IET Renewable Power Generation, 15(4): 794–808. Panda, DK., and Das, S. 2021. Smart grid architecture model for control, optimization and data analytics of future power networks with more renewable energy. Journal of Cleaner Production, 301: 126877. https://doi.org/10.1016/j.jclepro.2021.126877 Panda, M., and Nayak, YK. 2022. Impact analysis of renewable energy Distributed Generation in deregulated electricity markets: A context of Transmission Congestion Problem. Energy, 254:124403. Paul, K., Kumar, N., and Dalapati, P. 2021. Bat algorithm for congestion alleviation in power System network. Technology and Economics of Smart Grids and Sustainable Energy, 6:1–18. http://www.azojete.com.ng/ mailto:zainabobaone@gmail.com https://doi.org/10.1016/j.apenergy.2020.115058 https://doi.org/10.1155/2023/5395658 https://doi.org/10.1016/j.renene.2019.10.144 Arid Zone Journal of Engineering, Technology and Environment, December 2025; Vol. 21(4): 1039-1053. ISSN 1596-2490; e-ISSN2545-5818; www.azojete.com.ng Corresponding author’s email address: zainabobaone@gmail.com 1052 Pinzón, JD., and Colomé, DG. 2019. Real-time multi-state classification of short-term voltage stability based on multivariate time series machine learning. International Journal of Electrical Power & Energy Systems, 108: 402–414. Purlu, M., and Turkay, BE. 2022. Optimal Allocation of Renewable Distributed Generations Using Heuristic Methods to Minimize Annual Energy Losses and Voltage Deviation Index. IEEE Access, 10: 21455–21474. https://doi.org/10.1109/ACCESS.2022.3153042 Rao, NT., Kumar, KK., Kumar, PP., Nuvvula, RSS., Mutharasan, A., Dhanamjayulu, C., Shaik,MR., and Khan, B. 2024. Multiobjective optimal TCSC placement using multiobjective grey wolf optimizer for power losses reduction. Scientific Reports, 14(1): 21857. Reddy, R. 2021. Power Loss Minimization in a Transmisssion System by Optimally identifying various FACTS Devices using HGAPSO Heuristic Method. I-Manager’s Journal on Future Engineering and Technology, 16(3). Reddy, SS. 2020. Valtage Stability Enhancement and Power Losses Reduction ia a Transmission System by Optimally identifying various FACTS Devices. I-Manager’s Journal on Power Systems Engineering, 8(4). Roustaei, M., Letafat, A., Sheikh, M., Sadoughi, R., and Ardeshiri, M. 2022. A cost-effective voltage security constrained congestion management approach for transmission system operation improvement. Electric Power Systems Research, 203: 107674. Sadiq, EH., and Antar, RK. 2024. Minimizing power losses in distribution networks: A comprehensive review. Chinese Journal of Electrical Engineering, 10(4): 20–36. Sambaiah, KS., and Jayabarathi, T. 2020. Loss minimization techniques for optimal operation and planning of distribution systems: A review of different methodologies. International Transactions on Electrical Energy Systems, 30(2): e12230. Sarwar, M., Siddiqui, AS., Ghoneim, SSM., Mahmoud, K., and Darwish, MMF. 2022. Effective transmission congestion management via optimal DG capacity using hybrid swarm optimizationfor contemporary power system operations. IEEE Access, 10: 71091–71106. Sekhavatmanesh, H., and Cherkaoui, R. 2018. Distribution network restoration in a multiagent framework using a convex OPF model. IEEE Transactions on Smart Grid, 10(3): 2618–2628. Shafiul Alam, M., Al-Ismail, FS., Salem, A., and Abido, MA. 2020. High-level penetration of renewable energy sources into grid utility: Challenges and solutions. IEEE Access, 8: 190277–190299. https://doi.org/10.1109/ACCESS.2020.3031481 Sharma, G., Bokoro, PN., Çelik, E., Bekiroglu, E. 2023. Power loss minimization through reconfiguration Using BPSO-based technique. Electric Power Components and Systems, 51(18):2148–2158. Shuaibu HA., Sun, Y., and Wang, Z. 2020. Optimization techniques applied for optimal planning and integration of renewable energy sources based on distributed generation: Recent trends. Cogent Engineering, 7(1). https://doi.org/10.1080/23311916.2020.1766394 Sifakis, N., Konidakis, S., and Tsoutsos, T. 2021. Hybrid renewable energy system optimum design and smart dispatch for nearly Zero Energy Ports. Journal of Cleaner Production, 310. https://doi.org/10.1016/j.jclepro.2021.127397 Sinsel, SR., Riemke, RL., and Hoffmann, VH. 2020. Challenges and solution technologies for the integration of variable renewable energy sources—a review. Renewable Energy, 145: 2271–2285. Suresh, V., Sreejith, S., Sudabattula, SK., and Kamboj, VK. 2019. Demand response-integrated economic dispatch incorporating renewable energy sources using ameliorated dragonfly algorithm. Electrical Engineering, 101(2): 421–442. https://doi.org/10.1007/s00202-019-00792-y http://www.azojete.com.ng/ mailto:zainabobaone@gmail.com https://doi.org/10.1109/ACCESS.2022.3153042 https://doi.org/10.1109/ACCESS.2020.3031481 https://doi.org/10.1080/23311916.2020.1766394 https://doi.org/10.1016/j.jclepro.2021.127397 https://doi.org/10.1007/s00202-019-00792-y Arid Zone Journal of Engineering, Technology and Environment, December 2025; Vol. 21(4): 1039-1053. ISSN 1596-2490; e-ISSN2545-5818; www.azojete.com.ng Corresponding author’s email address: zainabobaone@gmail.com 1053 Tomar, A. 2024. Congestion management techniques in PV Rich LV distribution grids-a structured review. Energy Systems, 15(4): 1561–1593. Ushashree, P., and Kumar, KS. 2023. Power system reconfiguration in distribution system for loss minimization using optimization techniques: a review. Wireless Personal Communications, 128(3): 1907–1940. Waleed, U., Haseeb, A., Ashraf, MM., Siddiq, F., Rafiq, M., and Shafique, M. 2022. A Multiobjective Artificial- Hummingbird-Algorithm-Based Framework for Optimal Reactive Power Dispatch Considering Renewable Energy Sources. Energies, 15(23). https://doi.org/10.3390/en15239250 Wang, N., Li, J., Hu, W., Zhang, B., Huang, Q., and Chen, Z. 2019. Optimal reactive power dispatch of a full- scale converter based wind farm considering loss minimization. Renewable Energy, 139: 292–301. https://doi.org/10.1016/j.renene.2019.02.037 Wang, Q., Wan, J., and Yuan, Y. 2018. Locality constraint distance metric learning for traffic congestion detection. Pattern Recognition, 75: 272–281. Wang, W., Sun, B., Li, H., Sun, Q., and Wennersten, R. 2020. An improved min-max power dispatching method for integration of variable renewable energy. Applied Energy, 276. https://doi.org/10.1016/j.apenergy.2020.115430 Worighi, I., Maach, A., Hafid, A., Hegazy, O., and Van Mierlo, J. 2019. Integrating renewable energy in smart grid system: Architecture, virtualization and analysis. Sustainable Energy, Grids and Networks, 18: 100226. https://doi.org/10.1016/j.segan.2019.100226 Xiong, C., Liu, H., Yang, Z., and Wang, J. 2022. Optimal Reactive Power Dispatch of Distribution Network Considering Voltage Security. IEEE Symposium Series on Computational Intelligence (SSCI), 1362–1367. Xu, Y., Dong, Z., Li, Z., Liu, Y., and Ding, Z. 202). Distributed optimization for integrated frequency regulation and economic dispatch in microgrids. IEEE Transactions on Smart Grid, 12(6): 4595–4606. Yan, X., Wang, Q., and Bu, J. 2024. High penetration PV active distribution network power flow optimization and loss reduction based on flexible interconnection technology. Electric Power Systems Research, 226: 109839. Yang, Y., Yang, P., Zhao, Z., and Lai, LL. 2023. A multi-timescale coordinated optimization framework for economic dispatch of micro-energy grid considering prediction error. IEEE Transactions on Power Systems, 39(2): 3211–3226. Yousefi, A., Nguyen, TT., Zareipour, H., and Malik, OP. 2012. Congestion management using demand response and FACTS devices. International Journal of Electrical Power & Energy Systems, 37(1): 78–85. Zhang, R., Xu, Y., Dong, ZY., and Wong, KP. 2015. Post-disturbance transient stability assessment of power systems by a self-adaptive intelligent system. IET Generation, Transmission and Distribution, 9(3): 296–305. Zhang, X., Xu, Z., Yu, T., Yang, B., and Wang, H. 2020. Optimal mileage based AGC dispatch of a GenCo. IEEE Transactions on Power Systems, 35(4): 2516–2526. Zhao, T., Yan, H., Liu, X., and Ding, Z. 2022. Congestion-aware dynamic optimal traffic power flow in coupled transportation power systems. IEEE Transactions on Industrial Informatics, 19(2): 1833–1843. Zubidi, AN., Ismail, B., Al Hamrounni, IMA., Rahman, NHA., and Mohd Rozlan, MHH. 2023. The Impact of Integrating Multi-Microgrid System with FACTS Devices for Voltage Profile Enhancement and Real Power Loss Reduction in Power System: A Review. Pertanika Journal of Science & Technology, 31(2). http://www.azojete.com.ng/ mailto:zainabobaone@gmail.com https://doi.org/10.3390/en15239250 https://doi.org/10.1016/j.renene.2019.02.037 https://doi.org/10.1016/j.apenergy.2020.115430 https://doi.org/10.1016/j.segan.2019.100226