Multi‐time hierarchical stochastic predictive control for energy management of an island microgrid with plug‐in electric vehicles

Zou, Yuanyuan; Dong, Yi; Li, Shaoyuan; Niu, Yugang

doi:10.1049/iet-gtd.2018.5332

Cited by 28 publications

(12 citation statements)

References 23 publications

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“…LC is used as an interface for controllable loads through local load shedding in emergencies and MC controls active and reactive power generation levels of the micro-sources. The exchanged information between network controllers includes basic commands such as the operating points of active and reactive powers and voltage level information of LC and MC [40].…”

Section: Microgrid Central Controller (Mgcc)mentioning

confidence: 99%

Investigation of Microgrid Hierarchical Control and Structure

Faghihi¹,

Sabzi²

2020

AMA_C

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This paper offers a literature review on different structures and control strategies of the microgrid. The implementation of a microgrid system provides significant advantages for both electric utility and end-users. Microgrid control is one of the most sophisticated parts of such implementations that must be taken into account before moving to the next steps. The different control objectives and structures of the main grid and microgrid lead to various control methods proposed for microgrids. The hierarchical structure is the most accepted method for microgrid control that has three levels including primary, secondary, and tertiary. The focus of this study will be on the main modes of typical microgrids, types of microgrid and how to control them. The investigation of this paper will give an overview to the readers, and in our next paper, we will focus more deeply on the subject, with a specific concentration on the concept of secondary control and corresponding trending methods.

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Section: Microgrid Central Controller (Mgcc)mentioning

confidence: 99%

Investigation of Microgrid Hierarchical Control and Structure

Faghihi¹,

Sabzi²

2020

AMA_C

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“…The social utility of the MV microgrid is defined as the profit of LV distribution networks minus the cost of generation. The objective of the microgrid is to maximize the social utility, which is shown in Equation (8). In (8), the power flow constraints are also considered.…”

Section: Social Utility Maximizationmentioning

confidence: 99%

“…This motivates the development of the microgrid. Actually, the microgrid [7][8][9][10] is a small-scale power distribution system, which is composed of distributed power sources and neighbor loads. When compared with the traditional power grid, the microgrid has several advantages.…”

Section: Introductionmentioning

confidence: 99%

Hour-Ahead Energy Trading Management with Demand Forecasting in Microgrid Considering Power Flow Constraints

2019

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Multiple small-scale low-voltage distribution networks with distributed generators can be connected in a radial pattern to form a multi-bus medium voltage microgrid. Additionally, each bus has an independent operator that can manage its power supply and demand. Since the microgrid operates in the market-oriented mode, the bus operators aim to maximize their own benefits and expect to protect their privacy. Accordingly, in this paper, a distributed hour-ahead energy trading management is proposed. First, the benefit optimization problem of the microgrid is solved, which is decomposed into the local benefit optimization sub problems of buses. Then, the local sub problems can be solved by the negotiation of operators with their neighbors. Additionally, the reference demand before negotiation is forecasted by the neural network rather than given in advance. Furthermore, the power flow constraints are considered to guarantee the operational stability. Meanwhile, the power loss minimization is considered in the objective function. Finally, the demonstration and simulation cases are given to validate the effectiveness of the proposed hour-ahead energy trading management.

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“…The reference [27] proposed a real time control scheme for a smart EV fleet for optimal coordination of vehicle-to-grid and grid-to-vehicle modes. In [28], the EV fleet is considered as a mobile storage system in a hierarchical real time model predictive control for MGs. The authors in [29] considered the control of a hybrid AC-DC MG in which the EV charging system is directly connected to the DC side.…”

Section: Introductionmentioning

confidence: 99%

DC Nanogrids for Integration of Demand Response and Electric Vehicle Charging Infrastructures: Appraisal, Optimal Scheduling and Analysis

et al. 2021

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With the development of electronic infrastructures and communication technologies and protocols, electric grids have evolved towards the concept of Smart Grids, which enable the communication of the different agents involved in their operation, thus notably increasing their efficiency. In this context, microgrids and nanogrids have emerged as invaluable frameworks for optimal integration of renewable sources, electric mobility, energy storage facilities and demand response programs. This paper discusses a DC isolated nanogrid layout for the integration of renewable generators, battery energy storage, demand response activities and electric vehicle charging infrastructures. Moreover, a stochastic optimal scheduling tool is developed for the studied nanogrid, suitable for operators integrated into local service entities along with the energy retailer. A stochastic model is developed for fast charging stations in particular. A case study serves to validate the developed tool and analyze the economical and operational implications of demand response programs and charging infrastructures. Results evidence the importance of demand response initiatives in the economic profit of the retailer.

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Multi‐time hierarchical stochastic predictive control for energy management of an island microgrid with plug‐in electric vehicles

Cited by 28 publications

References 23 publications

Investigation of Microgrid Hierarchical Control and Structure

Investigation of Microgrid Hierarchical Control and Structure

Hour-Ahead Energy Trading Management with Demand Forecasting in Microgrid Considering Power Flow Constraints

DC Nanogrids for Integration of Demand Response and Electric Vehicle Charging Infrastructures: Appraisal, Optimal Scheduling and Analysis

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