In June, Thermo Systems completed the Site Acceptance Testing for the Reef Island Energy Transfer Stations located in Manama in the Kingdom of Bahrain.
Thermo Systems, under a design build contract with Tabreed Bahrain, designed, built, delivered, and commissioned a network of 13 new Allen Bradley PLC based Energy Transfer Stations serving the Reef Island development. Reef Island is a man made land structure on the North Shore of Manama, the capital of the Kingdom of Bahrain. Each building is served with chilled water through a network of underground pipes that are connected to the building''s Energy Transfer Station, or ETS. The ETS regulates and meters the building''s cooling supply
Since 2005, Thermo Systems has provided automation design, consulting, systems integration, and automation construction services in the Kingdom of Bahrain.
Hudson Yards, the largest private development in the United States []
A significant and noteworthy aspect across the references lies in their distinct approaches to modeling microgrid optimization. Within this body of literature, it becomes evident that each reference tailors a specific model—be it linear, non-linear, convex, or otherwise— to address the unique intricacies and challenges inherent in microgrid optimization. Moreover, these references present an array of methodologies and solution techniques that encompass evolutionary optimization, mathematical optimization, and analytical and heuristic methods. This diversity in modeling and solution strategies underscores the rich tapestry of approaches available for tackling the multifaceted landscape of microgrid optimization.
Our paper is crucial because it stands out as a comprehensive and unique contribution in the field of microgrid optimization and management. In contrast to recent literature, our study utilizes a Mixed Integer Nonlinear Programming (MINLP) model and employs the Large-Scale Two-Population Algorithm (LSTPA) to address a wide array of objectives, including D-FACTS implementation, multi-objective (MO) optimization, energy storage system (ESS) operation, consideration of stochastic elements, and the integration of electric vehicles (EV) and demand-side management (DSM). This approach sets your research apart by seamlessly combining various components, making it an invaluable resource for achieving efficient, sustainable, and resilient microgrid operations.
D-FACT, EV, and ESS constitute pivotal components of the modern power grid, and comprehending their impact is integral to this research. To furnish a thorough analysis of the topic, it is imperative to consider various factors influencing the outcomes. By incorporating D-FACT, ESS, EV, and other pertinent variables, this study encompasses a broad spectrum of factors potentially affecting the results. Prior studies and existing literature have underscored the substantial influence of these factors on the outcomes under investigation. Therefore, their inclusion in the study is academically and empirically justified.
This study makes significant contributions in the following ways:
Introduction of a tailored Mixed Integer Nonlinear Programming (MINLP)-based model: This model is specifically designed to address the optimal operation of microgrids, considering multiple objectives, such as optimizing EV and ESS charging and discharging schedules and incorporating Distributed Flexible AC Transmission System (D-FACTS) devices.
Novel approach based on the Large-Scale Two-Population Algorithm (LSTPA): The proposed algorithm demonstrates exceptional prowess in handling intricate optimization problems and is particularly adept at managing large-scale microgrid scenarios.
Comprehensive analysis and evaluation: The research subjects the proposed algorithm and model to rigorous analysis using a 33-node microgrid across various test cases, showcasing remarkable performance compared to existing methodologies. This highlights the superior outcomes achieved in microgrid operations with the proposed solution.
Advancing microgrid management efficiency and effectiveness: The innovative solution presented in the research holds significant promise for advancing the efficiency and effectiveness of microgrid management in the face of increasing complexities of optimization problems.
This study is structured into four sections, each addressing crucial aspects of the research. Section 2 introduces the proposed model and its corresponding solution method. In Sect. 3, a comprehensive analysis of the numerical results obtained from the model is presented. Finally, Sect. 4 comprises conclusive findings drawn from the study, alongside valuable suggestions for the continuation of research in this domain.
This section begins by elucidating the proposed optimization model, encompassing the multi-objective function and the governing constraints of the problem. Subsequently, the section proceeds to introduce the proposed algorithm devised to solve this model effectively.
Equations (8) and (9) exemplify the equilibrium between active and reactive power within the microgrid, respectively. These equations serve to showcase the interplay and harmonious distribution of both active and reactive power in the microgrid.
Equations (10) and (11) delineate the thresholds for the total active and reactive loads altered within the microgrid, respectively. These equations provide valuable insights into the maximum allowable levels of both active and reactive loads that the microgrid can accommodate. Understanding these limits ensures the proper functioning and stability of the microgrid under various operating conditions.
Constraints (12) and (13) outline the permissible range of variations in active and reactive loads, respectively. These constraints shed light on the acceptable fluctuations that can occur in both active and reactive power within the microgrid without compromising its stability and performance.
Moreover, the boundaries of real and reactive power flow in the lines are elucidated by Eqs. (14) and (15) correspondingly. These equations play a crucial role in understanding the maximum limits of power transmission (Seyyedi et al. 2023) through the lines while maintaining the system''s operational integrity and safeguarding against overloading issues. By carefully observing these limits, the microgrid can operate efficiently and reliably, ensuring seamless power distribution and grid resilience.
The restriction on microgrid node voltage is depicted by Eq. (16). This particular equation serves as a crucial indicator of the allowable voltage levels at the microgrid nodes to ensure stable and consistent electrical operation. Additionally, Eq. (17) is employed to calculate the square of the node voltage within the microgrid. This calculation is pivotal in understanding the magnitude of the voltage at specific nodes, enabling us to analyze voltage profiles and assess potential voltage stability concerns. By utilizing these equations, the microgrid''s voltage control and management can be optimized, ensuring a well-regulated and reliable electrical distribution system.
Equation (18) illustrates the operational threshold of distributed generation, serving as a pivotal reference for optimizing its performance within the microgrid. By adhering to this operating limit, the distributed generation sources can effectively contribute to the grid while maintaining their stability and efficiency.
The boundaries for charging and discharging the Energy Storage Systems (ESSs) are outlined in Eqs. (19) and (20), respectively. These equations play a vital role in regulating the charge and discharge rates of the ESSs, ensuring that they operate within safe and optimal parameters. Proper management of ESS charging and discharging limits is essential to enhance grid resilience and store surplus energy for future use.
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