Model predictive real-time architecture for secondary voltage control of microgrids

Escobar, Eros; Betancur, Daniel; Manrique, Tatiana; Isaac, Idi A.

doi:10.1016/j.apenergy.2023.121328

Cited by 5 publications

(3 citation statements)

References 22 publications

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“…The Raspberry Pi gathers all data and prepares it for transmission. An InfluxDB database is created to store and analyze the acquired data [28], [29]. The Raspberry Pi connects to WiFi and provides data to the InfluxDB database and to the other MGs.…”

Section: One Microgrid System's Power Managementmentioning

confidence: 99%

Multi-microgrids system’s resilience enhancement

Chalah,

Belaidi

2024

IJEECS

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Nowadays, electricity consumption is increasing rapidly which leads to conventional power systems exhaustion. Therefore, micro-grids (MGs) implantation can enhance the resilience of power systems by implication of new resources, such as renewable energy sources (solar panel and wind power systems), electric vehicles (EV), and energy storage systems (ESS). This paper proposes a new strategy for optimal power consumption inside one microgrid; then, the approach will be extended to optimize the power consumption to enhance the resilience in the case of multi-MGs systems. The system controller of each microgrid has been implemented using ESP32 microcontroller and Raspberry IP4. The proposed approach intends to enhance the resilience of the system to react to any contingency in the system such as loss of power linkage between MG and the network in case of any natural disaster, especially in the rural area. Two controllers are implemented; the first one ensures MG autonomy by the efficient use of its own sources. The second one handles the system resilience cases by demanding/delivering power from/into neighbor microgrids. Hence, this work enhances the system resilience with an optimal cost. Thus, the MG can offer ancillary services for the neighboring MGs.

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Section: One Microgrid System's Power Managementmentioning

confidence: 99%

Multi-microgrids system’s resilience enhancement

Chalah,

Belaidi

2024

IJEECS

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“…A centralized hierarchical EMS was built for the campus microgrids, which consists of primary (local voltage and current management of DERs), secondary (voltage, frequency, power regulation, and optimal energy dispatch), and tertiary (mode transition and external grid relation) control levels, guided by [28]. The main control and management functions of the developed EMS are a model predictive voltage control strategy based on reactive power regulation described in [29] and implemented in [30], and the optimal active power dispatch architecture described in the present work.…”

Section: Introductionmentioning

confidence: 99%

Optimal Power and Battery Storage Dispatch Architecture for Microgrids: Implementation in a Campus Microgrid

Escobar,

Betancur,

Isaac

2024

Smart Grids and Energy

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The expansion of electric microgrids has led to the incorporation of new elements and technologies into the power grids, carrying power management challenges and the need of a well-designed control architecture to provide efficient and economic access to electricity. This paper presents the development of a flexible hourly day-ahead power dispatch architecture for distributed energy resources in microgrids, with cost-based or demand-based operation, built up in a multi-class Python environment with SQLExpress and InfluxDB databases storing the dispatcher and microgrid data, and its experimental implementation using Modbus communication. The experimental power dispatch architecture is described and each operation stage is detailed, including the considered mathematical models of the energy resources, the database management, the linear-programming optimization of power dispatch, and the Modbus setpoint writing. Validation studies of the proposed control system are presented for real-time digital-simulated devices and physical resources as a real application at the Universidad Pontificia Bolivariana (UPB) campus microgrid. The simulated and physical microgrid characteristics are described and the hourly dispatch results for generation, storage and load devices are presented, standing out as a reliable power management architecture for economic commitment and load peak shaving in simulated and real microgrids. The proposed architecture demonstrates its readiness for present and future electrical system challenges, effectively incorporating meteorological variations, renewable energy sources, and power demand fluctuations into the control framework, with a strong dependence on the quality of the meteorological forecast.

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“…To still be able to compute solutions efficiently, Lyapunov optimization is often used as an online control approach for batteries to reduce peaks in demand or PV feed-in [1,153,221,222]. Other approaches use model predictive control or reinforcement learning to incorporate additional aspects, such as ancillary services or voltage constraints into real-time control schemes [75,188,208]. Furthermore, in [247], a mixture between load curtailment and battery usage is proposed and in [91] a real-time P2P energy market is considered, which is solved using a novel online ADMM approach.…”

Section: -Introductionmentioning

confidence: 99%

Local energy trading for microgrids : modeling human behavior, uncertainty and grid constraints

Hönen

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Model predictive real-time architecture for secondary voltage control of microgrids

Cited by 5 publications

References 22 publications

Multi-microgrids system’s resilience enhancement

Multi-microgrids system’s resilience enhancement

Optimal Power and Battery Storage Dispatch Architecture for Microgrids: Implementation in a Campus Microgrid

Local energy trading for microgrids : modeling human behavior, uncertainty and grid constraints

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