Energy management system of networked microgrids through optimal reliability-oriented day-ahead self-healing scheduling

Ahmadi, Seyed Ehsan; Rezaei, Navid; Khayyam, Hamid

doi:10.1016/j.segan.2020.100387

Cited by 31 publications

(9 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To solve the optimization problem for MPC, the constraints introduced by mathematical models of batteries and renewable energy elements significantly affect the performance of the optimization algorithm [13]. This problem is very common for MPC, as this control strategy approximates the optimal infinite-horizon continuous-time domain control by repeatedly solving an optimal finite-horizon discrete-time open-loop control [38,39]. We can conclude that for this paper, an MPC with a longer horizon and a shorter sampling time is better since the optimization is completed in a sufficiently short time to be considered in real-time [40].…”

Section: Model-based Predictive Control (Mpc)mentioning

confidence: 99%

Optimal Management of a Microgrid with Radiation and Wind-Speed Forecasting: A Case Study Applied to a Bioclimatic Building

et al. 2021

View full text Add to dashboard Cite

An Energy Management System (EMS) that uses a Model Predictive Control (MPC) to manage the flow of the microgrids is described in this work. The EMS integrates both wind speed and solar radiation predictors by using a time series to perform the primary grid forecasts. At each sampling data measurement, the power of the photovoltaic system and wind turbine are predicted. Then, the MPC algorithm uses those predictions to obtain the optimal power flows of the microgrid elements and the main network. In this work, three time-series predictors are analyzed. As the results will show, the MPC strategy becomes a powerful energy management tool when it is integrated with the Double Exponential Smoothing (DES) predictor. This new scheme of integrating the DES method with an MPC presents a good management response in real-time and overcomes the results provided by the Optimal Power Flow method, which was previously proposed in the literature. For the case studies, the test microgrid located in the CIESOL bioclimatic building of the University of Almeria (Spain) is used.

show abstract

Section: Model-based Predictive Control (Mpc)mentioning

confidence: 99%

Optimal Management of a Microgrid with Radiation and Wind-Speed Forecasting: A Case Study Applied to a Bioclimatic Building

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Bi-level programming needed information iteration between attackers and schedulers, which enhanced the complexity of the system. Ahmadi et al (2020) studied the schedulable distributed generator and emergency load shedding self-healing scheduling method of MMG. The upper EMS was responsible for the optimization of MMG, and the lower EMS was responsible for the self-healing of MG in case of fault.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Optimal Scheduling of Multi-Microgrids Considering Renewable Energy Intermittency

Wang

2022

Front. Energy Res.

View full text Add to dashboard Cite

Accelerating the penetration of renewable energy (RE) in energy consumption is an important method to realize the promotion of CO2 emission peaking and carbon neutrality. The energy transaction between two microgrids (MGs) makes up for the limitations that a single MG cannot deal well with the intermittence and fluctuation of RE in the real-time scheduling of the system. Multi-microgrids (MMGs) composed of multiple MGs have become an effective supplement to China’s power system. However, extreme weather and natural disasters can easily cause fault shutdown of wind turbines (WTs) and photovoltaics (PVs) in the microgrid (MG). To better balance the flexible load curtailment and satisfaction of MMGs, this paper proposes a coordinated scheduling model for MMGs. This model covers the WT, the PV, the fuel cell (FC), the energy storage system (ESS), and flexible load curtailment. First, the energy management system (EMS) of MMGs collects information on all the distributed generators’ output and three types of loads. The contribution bargaining game is applied to realize the energy transaction between each two MGs. Second, balancing multi-microgrid satisfaction and the profit of each MG is taken as the objective function, and the scheduling strategy of each MG is formulated. Also, an improved optimization method is applied to solve the amount of flexible load curtailment of each MG and realize the reasonable scheduling of MMG in the fault state. In the case study, the superiority of the model and the proposed method has been verified.

show abstract

“…Although EMS in NMG is more complicated in comparison with individual MG, the flexibility of energy sharing amongst several MGs can offer extra benefits to the system. Increasing the reliability of the system, specifically in an isolated operation mode, and the possibility of power management in an interactive manner between MGs to provide the demand are some of the advantages that can be gained in NMG [4,5].…”

Section: Introductionmentioning

confidence: 99%

Networked Microgrid Energy Management Based on Supervised and Unsupervised Learning Clustering

2022

View full text Add to dashboard Cite

Networked microgrid (NMG) is a novel conceptual paradigm that can bring multiple advantages to the distributed system. Increasing renewable energy utilization, reliability and efficiency of system operation and flexibility of energy sharing amongst several microgrids (MGs) are some specific privileges of NMG. In this paper, residential MGs, commercial MGs, and industrial MGs are considered as a community of NMG. The loads’ profiles are split into multiple sections to evaluate the maximum load demand (MLD). Based on the optimal operation of each MG, the operating reserve (OR) of the MGs is calculated for each section. Then, the self-organizing map as a supervised and a k-means algorithm as an unsupervised learning clustering method is utilized to cluster the MGs and effective energy-sharing. The clustering is based on the maximum load demand of MGs and the operating reserve of dispatchable energy sources, and the goal is to provide a more efficient system with high reliability. Eventually, the performance of this energy management and its benefits to the whole system is surveyed effectively. The proposed energy management system offers a more reliable system due to the possibility of reserved energy for MGs in case of power outage variation or shortage of power.

show abstract

Energy management system of networked microgrids through optimal reliability-oriented day-ahead self-healing scheduling

Cited by 31 publications

References 42 publications

Optimal Management of a Microgrid with Radiation and Wind-Speed Forecasting: A Case Study Applied to a Bioclimatic Building

Optimal Management of a Microgrid with Radiation and Wind-Speed Forecasting: A Case Study Applied to a Bioclimatic Building

Real-Time Optimal Scheduling of Multi-Microgrids Considering Renewable Energy Intermittency

Networked Microgrid Energy Management Based on Supervised and Unsupervised Learning Clustering

Contact Info

Product

Resources

About