Centralized Control Strategies
Centralized control systems are characterized by a central controller (CC) that collects data from various system entities and makes decisions based on a global perspective. This approach enables efficient grid operation, providing strong controllability and real-time observability of the entire microgrid (MG) system. The CC leverages high-performance computation and secure communication infrastructure to optimize performance.
However, the centralized approach also faces scalability and reliability challenges. The concentration of computational tasks and reliance on a single unit for voltage regulation can limit system flexibility. Additionally, the risk of system-wide operational disruptions and the heavy computational burden on the CC can pose significant drawbacks.
Numerous studies have explored centralized energy management (EM) strategies, showcasing diverse approaches and methodologies. For instance, Lin et al. (2015) introduces a MG EM strategy integrating renewable energy (RE) and battery storage systems, employing an enhanced bee colony optimization (EBCO) algorithm for optimal scheduling and dispatch. Similarly, Tsikalakis and Hatziargyriou (2011) discuss a central controller for MGs that optimizes interconnected operation by managing local distributed generation (DG) production and power exchanges with the main grid.
Decentralized Control Strategies
In contrast, decentralized control in MGs allows autonomous entities to manage subsystems independently, reducing computational complexity and enhancing system responsiveness. This approach leverages local measurements and peer-to-peer communication, promoting operational flexibility and fault tolerance.
However, decentralized control faces challenges in global optimization and synchronization among distributed entities, such as limited information exchange, load dependency issues, and harmonics. Implementing distributed processing requires careful coordination and may introduce complexity compared to centralized approaches.
Many studies have explored decentralized EM strategies, showcasing diverse approaches and methodologies. For example, Zheng et al. (2018) used both deterministic constrained optimization and stochastic optimization to examine the uncertainties in biomass-integrated MGs, demonstrating the cost-effectiveness of combining a biomass-combined heat and power-photovoltaic (BCHPPV) system with battery storage. Additionally, Kuznetsova et al. (2015) looked at an MG EM framework that uses robust optimization (RO) and prediction intervals to address uncertainties in wind power generation and consumption, emphasizing the importance of unknown events for MG performance and reliability.
Hierarchical Control Strategies
Hierarchical control in smart grid systems is crucial due to their expansive geographical coverage and communication demands. This approach encompasses three main levels:
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Primary level: Responsible for local power, voltage, and current control, employing interface power converters (PCs) to execute control actions based on upper-level setpoints. This level ensures immediate responses to local disturbances, maintaining voltage and frequency stability.
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Secondary level: Assumes a supervisory role, managing power quality control within the MG. This level focuses on tasks such as voltage and frequency restoration, voltage unbalance correction, and power exchange with the main grid or other interconnected MGs.
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Tertiary level: Introduces intelligence and optimization into the entire MG system, aiming to optimize operation based on various merits, primarily efficiency and economics. The tertiary controller utilizes advanced optimization algorithms and decision-making techniques to achieve optimal power flow, energy dispatch, and resource allocation.
The hierarchical control structure simplifies modeling and analysis, as each control level exhibits distinct bandwidths. As control levels ascend, regulation speed diminishes, with primary control responding within milliseconds, secondary control within seconds to minutes, and tertiary control executing discrete-time decision-making steps over seconds to hours.
Several studies have advanced hierarchical EM strategies, demonstrating their effectiveness in optimizing decentralized energy systems. For example, Ghaffari and Askarzadeh (2020) propose an efficient optimization approach for sizing hybrid power generation systems using RESs, highlighting the significant impact of RE penetration on total net present cost. Similarly, Sandgani and Sirouspour (2017) introduce a method for dispatching and sharing energy storage in grid-connected MGs, enabling cost-effective power transactions and reducing electricity costs through multi-objective optimization.
Simulation-Based and Real-Life Energy Management Controllers
Energy management controllers (EMCs) can be categorized into two main groups: those applied in real-life settings and those utilized in simulations.
Simulation-based EMCs encompass a diverse range of methodologies, including commercial software-based solutions, conventional techniques, rule-based systems, optimization algorithms, artificial intelligence (AI) approaches, and hybrid methods. These strategies are employed to model, analyze, and optimize EM processes in various contexts, offering insights into system behavior, performance, and efficiency.
On the other hand, real-life applications of EMCs involve the practical implementation of these controllers in a variety of contexts, such as smart grids, buildings, industrial processes, and transportation systems. These real-world implementations demonstrate the efficacy of different control strategies and architectures in addressing specific energy management challenges and achieving desired outcomes.
Commercial Software-Based EMCs
Commercial software applications are essential in energy management, offering functions like control strategies, simulation, technical analysis, economic optimization, and multi-objective optimization. Notable examples include HOMER, iHOGA, and HYBRID2.
HOMER is a user-friendly software developed by the National Renewable Energy Laboratory (NREL) that aids in the design and planning of hybrid renewable energy systems (HRES). It provides comprehensive economic and technical comparisons, suggesting load-serving policies with the most cost-effective energy source.
iHOGA, developed by the University of Zaragoza, is a powerful C program that accommodates electrical energy, hydrogen, and water loads. It excels at simulating and optimizing stand-alone hydrogen reactors and grid-connected systems, offering detailed control strategies and unique features like calculating the Human Development Index and job creation factor.
HYBRID2, a collaborative creation by the University of Massachusetts and the NREL, is a versatile and user-friendly tool for predicting the long-term performance of hybrid power systems. Its graphical user interface, libraries, and control strategies facilitate the modeling of various hybrid systems incorporating elements such as wind turbines, photovoltaic modules, diesel generators, battery storage, and connected loads.
Conventional and Advanced Control Strategies
Conventional EM methodologies, such as discrete-time self-tuning on-off controllers and proportional-integral (PI) controllers, have long served as the cornerstone for optimizing energy consumption and cost reduction strategies. However, these methods often lack adaptability and real-time insights, hindering the timely identification of energy trends and performance evaluation.
To address these challenges, recent studies have explored innovative control strategies to enhance EMSs. For instance, the integration of advanced algorithms, like model predictive control (MPC) and rule-based systems, has shown promise in optimizing energy efficiency across various domains, including smart grids, buildings, and industrial processes.
Heuristic controllers have also emerged as indispensable tools for optimizing EMSs, demonstrating their efficacy in scheduling home appliances, managing renewable resources, and mitigating peak-to-average ratio challenges. Techniques like genetic algorithms (GA), binary particle swarm optimization (BPSO), and ant colony optimization (ACO) have been widely applied in this context.
Moreover, the incorporation of AI and machine learning (ML) techniques, such as artificial neural networks (ANN), deep reinforcement learning (DRL), and fuzzy logic controllers (FLC), has offered substantial benefits across residential, commercial, and hybrid energy domains. These advanced approaches have shown their potential in optimizing energy consumption, enhancing system efficiency, and addressing complex EM challenges.
Hybrid Optimization and Real-Time Implementation
In addition to the aforementioned control strategies, researchers have explored the integration of hybrid optimization controllers to further enhance EMSs. These approaches combine various techniques, such as mixed-integer linear programming (MILP), MPC, and ensemble methods, to optimize hybrid MG operations, energy storage integration, and participation in electricity markets.
Furthermore, the implementation of EMCs in real-time settings has gained increasing attention. These real-time optimization and control mechanisms aim to improve energy efficiency and reliability, addressing challenges such as load management, RE integration, and power flow optimization across diverse applications, including smart grids, buildings, and transportation systems.
By leveraging a combination of advanced control strategies and real-time implementation, the energy management landscape continues to evolve, driving the transition towards more sustainable and efficient sensor network and IoT solutions. These innovations hold immense promise in shaping the future of energy optimization and management, contributing to a more resilient and eco-friendly energy infrastructure.
Conclusion
Energy management controllers (EMCs) play a crucial role in optimizing energy consumption and ensuring operational efficiency across a wide range of systems. This review has provided a comprehensive overview of various control strategies employed by EMCs, including centralized, decentralized, and hierarchical approaches, along with their coordination mechanisms and architectures.
The exploration of simulation-based and real-life EMCs has highlighted the diverse methodologies and practical applications of these controllers, showcasing their effectiveness in addressing specific energy management challenges. The review has also delved into the advancements in conventional and innovative control strategies, such as rule-based systems, heuristic algorithms, and AI/ML techniques, demonstrating their potential to enhance energy efficiency and sustainability.
Moreover, the integration of hybrid optimization controllers and real-time implementation of EMCs have emerged as promising avenues for further optimizing energy management in sensor networks and IoT applications. These developments hold significant implications for the future of energy optimization, driving the transition towards a more resilient and eco-friendly energy infrastructure.
By leveraging the insights and strategies outlined in this article, sensor network and IoT practitioners can harness the power of advanced energy management controllers to unlock new frontiers in efficient and sustainable energy solutions, ultimately shaping a brighter future for our interconnected world.