Smart transactive energy framework in grid-connected multiple home microgrids under independent and coalition operations

Marzband, Mousa; Azarinejadian, Fatemeh; Savaghebi, Mehdi; Pouresmaeil, Edris; Guerrero, Josep M.; Lightbody, Gordon

doi:10.1016/j.renene.2018.03.021

Cited by 185 publications

(99 citation statements)

References 36 publications

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“…Considering customer satisfaction, the total response time of DRA is enforced to be not larger than a specific response time limit in (21). In the meantime, constraint (22) requires that the interruptible power is only allowed to be scheduled in the allowable time set.…”

Section: Dra Constraintsmentioning

confidence: 99%

Hierarchical Energy Management of Microgrids including Storage and Demand Response

Fan

Piao

2018

Energies

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Battery energy storage (BES) and demand response (DR) are considered to be promising technologies to cope with the uncertainty of renewable energy sources (RES) and the load in the microgrid (MG). Considering the distinct prediction accuracies of the RES and load at different timescales, it is essential to incorporate the multi-timescale characteristics of BES and DR in MG energy management. Under this background, a hierarchical energy management framework is put forward for an MG including multi-timescale BES and DR to optimize operation with the uncertainty of RES as well as load. This framework comprises three stages of scheduling: day-ahead scheduling (DAS), hour-ahead scheduling (HAS), and real-time scheduling (RTS). In DAS, a scenario-based stochastic optimization model is established to minimize the expected operating cost of MG, while ensuring its safe operation. The HAS is utilized to bridge DAS and RTS. In RTS, a control strategy is proposed to eliminate the imbalanced power owing to the fluctuations of RES and load. Then, a decomposition-based algorithm is adopted to settle the models in DAS and HAS. Simulation results on a seven-bus MG validate the effectiveness of the proposed methodology.

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Section: Dra Constraintsmentioning

confidence: 99%

Hierarchical Energy Management of Microgrids including Storage and Demand Response

Fan

Piao

2018

Energies

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“…Lithium-ion (Li-ion) batteries are the key component of a growing body of energy-related applications [1], such as microgrids [2,3], electricity retailing market [4], uninterruptible power supplies (UPSs) [5], and electric vehicles (EVs) [6]. However, battery degradation exists anytime once produced and can be excessively accelerated if inappropriately managed.…”

Section: Introductionmentioning

confidence: 99%

Aging trajectory prediction for lithium-ion batteries via model migration and Bayesian Monte Carlo method

et al. 2019

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This paper develops a new prediction method for the aging trajectory of lithium-ion batteries with significantly reduced experimental tests. This method is driven by data collected from two types of battery operation modes. The first type is accelerated aging tests that are performed under stress factors, such as overcharging, over-discharging and large current rates, and cover most of the battery lifespan. In the second operation mode, the same kinds of cells are aged at normal speeds to generate a partial aging profile. An accelerated aging model is developed based on the first type of data and is then migrated as a new model to describe the normalspeed aging behavior. Under the framework of Bayesian Monte Carlo algorithms, the new model is parameterized based on the second type of data and is used for prediction of the remaining battery aging trajectory. The proposed prediction method is validated on three types of commercial batteries and also compared with two benchmark algorithms. The sensitivity of results to the number of cycles is investigated for both modes. Illustrative results demonstrate that based on the normal-speed aging data collected in the first 30 cycles, the proposed method can predict the entire aging trajectories (up to 500 cycles) at a root-mean-square error of less than 2.5% for all considered scenarios. When only using the first five-cycle data for model training, such a prediction error is bounded by 5% for aging trajectories of all the tested batteries.

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“…The ICT enables smart homes to communicate with both its internal devices, appliances (which it can control), and also with its surroundings. Furthermore, a smart home can adapt to grid's conditions and communicate with neighbour smart homes, thereby creating an active or a virtual power plant [9]- [12].…”

Section: Introductionmentioning

confidence: 99%

Simplified Algorithm for Dynamic Demand Response in Smart Homes Under Smart Grid Environment

Singh

Namboodiri

Selvan

2019

2019 IEEE PES GTD Grand International Conference and Exposition Asia (GTD Asia)

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Under Smart Grid environment, the consumers may respond to incentive-based smart energy tariffs for a particular consumption pattern. Demand Response (DR) is a portfolio of signaling schemes from the utility to the consumers for load shifting/shedding with a given deadline. The signaling schemes include Time-of-Use (ToU) pricing, Maximum Demand Limit (MDL) signals etc. This paper proposes a DR algorithm which schedules the operation of home appliances/loads through a minimization problem. The category of loads and their operational timings in a day have been considered as the operational parameters of the system. These operational parameters determine the dynamic priority of a load, which is an intermediate step of this algorithm. The ToU pricing, MDL signals, and the dynamic priority of loads are the constraints in this formulated minimization problem, which yields an optimal schedule of operation for each participating load within the consumer provided duration. The objective is to flatten the daily load curve of a smart home by distributing the operation of its appliances in possible low-price intervals without violating the MDL constraint. This proposed algorithm is simulated in MATLAB environment against various test cases. The obtained results are plotted to depict significant monetary savings and flattened load curves.

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Smart transactive energy framework in grid-connected multiple home microgrids under independent and coalition operations

Cited by 185 publications

References 36 publications

Hierarchical Energy Management of Microgrids including Storage and Demand Response

Hierarchical Energy Management of Microgrids including Storage and Demand Response

Aging trajectory prediction for lithium-ion batteries via model migration and Bayesian Monte Carlo method

Simplified Algorithm for Dynamic Demand Response in Smart Homes Under Smart Grid Environment

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