Distributed Optimal Energy Management in Microgrids

Shi, Wenbo; Xie, Xiaorong; Chu, Chi-Cheng; Gadh, Rajit

doi:10.1109/tsg.2014.2373150

Cited by 430 publications

(230 citation statements)

References 24 publications

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“…The wind and solar information required for carried out the study of the power generated for 24 hours of a day. The cost function of wind and solar generation is as in (1) [16] and it considers the operation, investment, and maintenance cost of the energy generation. The power generation of renewable energy sources for 24 hours during a day is shown in below Table.2.…”

Section: Solar and Wind Parametersmentioning

confidence: 99%

“…In the above equations, the local scheduling problem (13)(14)(15)(16)(17)(18), in the main objective function (13) to be optimised the total EVs cost and it execute charging and discharging throughout the day. The constraint (14) signifies the relationship between the total load in the interval and the individual EV charging power.…”

Section: Local Optimal Schedulingmentioning

confidence: 99%

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Optimal Scheduling of Micro Grid for Plug-In Electrical Vehicle

Kumar¹,

Kumar²

2018

IJET

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Owing to several advantages of Plug-in Electric Vehicles (PEVs) like less noise, emission less, good efficiency and the reduced cost has attention to the governments, researchers and manufactures in recent time. The Plug-in Electric Vehicle (PEV) plays a vital role in replacement of conventional vehicles in future, because of penetration of renewable energy resources in conventional generation. The modernized of micro grid is happening due to usage of clean energy for EV charging. The cost of electric vehicle charging is challenging issue in the development of plug-in electric vehicle. The coordination between renewable generation and conventional generation is very much needed in near future. The dynamic nature of renewable energy resources causes frequent interrupts in electric vehicle charging. The problem of nonlinear power generation with renewable resources is overcome by electric vehicle battery storage system which enables the EV battery to charge during low demand period and gets discharged into the micro grid during high demand periods. This paper developed an optimal schedule for stationary Plug-in Electric Vehicle charging in operation with micro grid. The obtained optimal schedule provides balance between active and reactive power in generation and load as well. The integration of renewable energy resources is achieved through solar, wind in Vehicle-2-Grid (V2G) approach which is used to safeguard to renewable energy resources by store additional energy produced during peak load period and feeding back to the micro grid during low load period. As a result the stable operation of the micro grid and EV charging with low cost is achieved in this paper.

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Section: Solar and Wind Parametersmentioning

confidence: 99%

Section: Local Optimal Schedulingmentioning

confidence: 99%

Optimal Scheduling of Micro Grid for Plug-In Electrical Vehicle

Kumar¹,

Kumar²

2018

IJET

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show abstract

“…Hierarchical solutions, where a central coordinator takes on a leadership role, can overcome these problems by forming a multi-agent system (MAS). For example parallel optimization is performed by agents in [4][5][6], while global information updates such as Lagrange multipliers and aggregated load profiles are updated by a central coordinator. And in [7][8][9][10] global information is aggregated by a central entity and then communicated to agents who then apply a game theoretic approach to solving the optimization.…”

Section: Introductionmentioning

confidence: 99%

Asynchronous consensus for optimal power flow control in smart grid with zero power mismatch

Millar

Jiang

2018

J. Mod. Power Syst. Clean Energy

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The heterogeneous nature of smart grid components and the desire for smart grids to be scalable, stable and respect customer privacy have led to the need for more distributed control paradigms. In this paper we provide a distributed optimal power flow solution for a smart distribution network with separable global costs, separable non-convex constraints, and inseparable linear constraints, while considering important aspects of network operation such as distributed generation and load mismatch, and nodal voltage constraints. An asynchronous averaging consensus protocol is developed to estimate the values of inseparable global information. The consensus protocol is then combined with a fully distributed primal dual optimization utilizing an augmented Lagrange function to ensure convergence to a feasible solution with respect to power flow and power mismatch constraints. The presented algorithm uses only local and neighbourhood communication to simultaneously find the mismatch between power generation, line loss and loads, to calculate nodal voltages, and to minimize distributed costs, leading to a completely distributed solution of the global problem. An IEEE test feeder system with a reasonable number of nodes is used to illustrate the proposed method and efficiency.

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“…The electric energy generated by photovoltaic (PV) and wind turbine (WT) possesses variability due to the impact of natural conditions [11]. Meanwhile, due to the complexity of the combined cooling, heating and power dispatch of micro gas turbines, the variety of operating modes of microgrids, and other factors, the difference between microgrids and traditional power systems brings special challenges to optimized dispatch within microgrids [12].…”

Section: Introductionmentioning

confidence: 99%

Multi-objective energy management system for DC microgrids based on the maximum membership degree principle

Wang

Meng

et al. 2017

J. Mod. Power Syst. Clean Energy

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With the increasing quantity of DC electrical equipment, DC microgrids have been paid more and more attention. This paper proposes an approach to multi-objective optimisation of an energy management system (EMS) for a DC microgrid that includes a hybrid energy storage system (HESS). The operating and maintenance cost and the loss of power supply probability (LPSP) of the system are used as optimisation targets. The power flows of all distributed generators (DGs) in the DC microgrid during operating period are optimized. Based on the improved differential evolution (DE) algorithm, and by using the multi-objective non-dominated sorting method and the maximum membership degree principle (MMDP) of fuzzy control, the overall satisfaction degree of Pareto solutions to power flow optimization can be obtained. Simulation results verify the effectiveness of the proposed EMS optimization scheme, which is able to achieve an effective trade-off between the economy and the reliability of microgrid operation.

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Distributed Optimal Energy Management in Microgrids

Cited by 430 publications

References 24 publications

Optimal Scheduling of Micro Grid for Plug-In Electrical Vehicle

Optimal Scheduling of Micro Grid for Plug-In Electrical Vehicle

Asynchronous consensus for optimal power flow control in smart grid with zero power mismatch

Multi-objective energy management system for DC microgrids based on the maximum membership degree principle

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