Economic dispatch in microgrids using multi-agent system

Cai, Niannian; Nga, Nguyễn Thị Thanh; Mitra, Joydeep

doi:10.1109/naps.2012.6336435

Cited by 32 publications

(20 citation statements)

References 25 publications

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“…This siginificantly improves calculation quality and speed. Generally, methods basing on consensus algorithms are good choices, such as expressed in [58][59][60][61] . Basic idea of consensus algorithm is making identical partial derives of the profit functions of all CGs through adjustment.…”

Section: Economic Dispatchmentioning

confidence: 99%

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A Review on Multi-Agent Technology in Micro-Grid Control

Ding

Yin

2018

Electron Sci Technol Appl

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Micro-Grid (MG) integrates renewable generation, storage devices and controllable generations, it provides efficent utilization of clean energy while keeping stable external characteristics. Capability of continuous power supply, high scalability and flexible operation modes can satifiy the current demand of joint operation of renewable generation and Macro-Grid, and will provide a solid foundation for smart grid technology in the future. Thus, MG is an excellent integration of renewable energy utilization with a bright future, Multi-Agent System (MAS) is a new hierarchical control platform and can completely cover all the devices within a MG, its flexible control modes meet the needs of various operations of MG, and the capability of distributed computing supports intelligent functions of MG in the future. Therefore, developing premium functions for MAS in MG control will promote the development of both MG and Smart Grid technologies. This paper reviews the current applications of MAS technology for MG both in basic and advanced control demands. For basic demands concerning safe operations for MG, functions of MAS are available, but a further improvement of performance is essential for future researches to increase penetration of MAS in MG control; For advanced demands, MAS should increase calculation speed to meet the complex need of MG. In the last part, the future focuses are also depicted.

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Section: Economic Dispatchmentioning

confidence: 99%

“…This leads the MG to a condition of minimal cost rate. In [58] , cost increments of CGs and profit increments of flexible loads are taken as consensus variables for the algorithm. MAS usually runs in distributed mode when implementing consensus algorithm, as can be seen from the aforementioned references.…”

Section: Economic Dispatchmentioning

confidence: 99%

A Review on Multi-Agent Technology in Micro-Grid Control

Ding

Yin

2018

Electron Sci Technol Appl

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“…Mid-peak À0.08 À0.14 0.05 Off-peak À0.07 À0.05 À0.12 a Winter: on-peak (7)(8)(9)(10)(11)(17)(18)(19); mid-peak (11)(12)(13)(14)(15)(16)(17); off-peak . Summer: on-peak (11)(12)(13)(14)(15)(16)(17); mid-peak (7)(8)(9)(10)(11)(17)(18)(19); offpeak .…”

Section: Time Intervalsmentioning

confidence: 99%

“…Summer: on-peak (11)(12)(13)(14)(15)(16)(17); mid-peak (7)(8)(9)(10)(11)(17)(18)(19); offpeak . In this case, there is no upper-level economic operation, and MGs are seen as independent active consumers in the DISCO who provides day-ahead wholesale market price signals, shown in Figure 11.…”

Section: Time Intervalsmentioning

confidence: 99%

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Bilevel economic operation of distribution networks with microgrid integration

Zhang

Zhao

et al. 2015

Journal of Renewable and Sustainable Energy

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Traditionally, the economic operation of a microgrid (MG) is independent with that of a distribution network (DN), and the electricity trade between them is simply based on contract prices or wholesale electricity market prices. In order to coordinate the benefits of DNs and MGs more effectively, a new economic operation model for DNs with MG is proposed in this paper modeled as a bilevel programming problem (BLPP). At the upper level, electric power from the gird and electricity price of MGs are control variables. A DN seeks the least operation cost by optimizing these control variables. At the lower level, control variables are electric power of dispatchable distributed generations (DGs) and the DN. Based on the electricity price got from the upper level and the operation costs of dispatchable DGs, a MG minimizes its hourly operation cost by optimizing the control variables. Furthermore, electricity price constraints are considered in this BLPP model, and a method to dynamically determine the constraints is proposed. They are decided by the load power demand profile in the DN and the price differential between the wholesale electricity market and dispatchable DGs. A comprehensive real-time pricing for residential consumers is also considered in the operation model to study the impact from demand response. The proposed BLPP model is solved by particles swarm optimization based on bilevel iterative algorithm. The demonstration is performed on a modified IEEE 33-node radial distribution system. V C 2015 AIP Publishing LLC. [http://dx.

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Microgrid Operations Under Electricity Market Dynamics

2023

The Economics of Microgrids

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Economic dispatch in microgrids using multi-agent system

Cited by 32 publications

References 25 publications

A Review on Multi-Agent Technology in Micro-Grid Control

A Review on Multi-Agent Technology in Micro-Grid Control

Bilevel economic operation of distribution networks with microgrid integration

Microgrid Operations Under Electricity Market Dynamics

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