Modeling Local Energy Market for Energy Management of Multi-Microgrids

Sheikhahmadi, Pouria; Bahramara, Salah; Shahrokhi, Saman; Chicco, Gianfranco; Mazza, Andrea; Catalào, João P. S.

doi:10.1109/upec49904.2020.9209891

Cited by 10 publications

(9 citation statements)

References 19 publications

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“…The other cost terms that can be included in the cost minimisation formulation of the MG are the operational cost of the DG [59], load interruption cost [101], and discomfort cost of flexible loads [96, 102].…”

Section: Level 3: Microgridsmentioning

confidence: 99%

“…In [57], it was formulated in terms of the cost of energy purchased from the main grid and the revenue of the energy sold to the main grid. The other cost terms that can be included in the cost minimisation formulation of the MG are the operational cost of the DG [59], load interruption cost [101], and discomfort cost of flexible loads [96,102].…”

Section: Level 3: Microgridsmentioning

confidence: 99%

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Transactive energy management systems: Mathematical models and formulations

Thangavelu

2023

Energy Conversion and Econom

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Restructuring the power system with higher penetration of distributed energy resources (DERs) and intelligent devices offers the potential for more efficient, reliable, and better resource utilisation of power systems through the transactive energy framework (TEF). This article provides a general overview of the mathematical models and formulations of the TEF reported in the literature. TEF concepts can be applied to various levels of the power system. Here, the TEF-related literature is divided into individual DER-, building-, microgrid-, and macrogrid-level TEF. The mathematical models of transactive agents corresponding to each level and power system network models are presented. Furthermore, TEF models for energy management and trading of integrated multi-energy systems are analysed. Finally, the potential challenges and future research directions for transactive energy are discussed.

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Section: Level 3: Microgridsmentioning

confidence: 99%

Section: Level 3: Microgridsmentioning

confidence: 99%

Transactive energy management systems: Mathematical models and formulations

Thangavelu

2023

Energy Conversion and Econom

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“…This model proposes a lower pricing cost and maintains good computational efficiency. A local market-based energy sharing model between multiple MG is developed in [16] to minimize the operational costs for each MG and among MGs. In this model, optimal scheduling of DG within MG is done using the market clearing process.…”

Section: Introductionmentioning

confidence: 99%

Flexible Boundary Multi-Microgrids Power Distribution Systems with Internet of Thing for System Efficiency Enhancement

Alam¹,

Arefifar²

2022

GJRE

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Multi-microgrid power distribution systems are gaining attention in the smart grid era. Distributed energy resources, energy storage, as well as energy sharing and scheduling has a great potential to enhance multi-microgrid systems’ performance. This research develops an algorithm for optimal operation of various distributed energy resources in a flexible boundary multi-microgrid power distribution network, considering internet of things (IoT). The proposed algorithm used in this research can reduce power system operating costs, power, and energy losses and emissions, and ultimately increase the systems’ efficiency. A hybrid Particle Swarm Optimization-Tabu Search algorithm is developed for optimization purposes.

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“…An aggregator (Fig. 1) is defined as a market agent that combines energy demand and generation to satisfy a portfolio of customers [11], [12]. In this case, users can self-supply, dump their surplus energy into the grid, and the aggregator is responsible for energy planning to supply its portfolio and obtain an economic return for it [13].…”

Section: Introductionmentioning

confidence: 99%

Optimisation of an aggregator’s customer portfolio based on inter-user compatibility

Cano-Martínez¹,

Peñalvo-López²,

León-Martínez³

et al. 2022

REPQJ

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This paper aims to present a methodology for optimizing the customers’ portfolio of an aggregator by analysing the compatibility between prosumers (clients). The methodology evaluates two main components: energy and socioeconomic. With this, the intention is to maximise the economic benefits of the aggregator and to establish a personalised tariff according to the user profile. Since this figure was conceived, efforts have been mainly aimed at meeting only and exclusively the energy demand requirements without taking into consideration prosumers socio-economic profiles and the synergies among them. This paper, therefore, presents a methodology in which a compatibility index based on the energy and socioeconomic profile of each client is defined. In addition, the application of this methodology to a practical case is presented.

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Modeling Local Energy Market for Energy Management of Multi-Microgrids

Cited by 10 publications

References 19 publications

Transactive energy management systems: Mathematical models and formulations

Transactive energy management systems: Mathematical models and formulations

Flexible Boundary Multi-Microgrids Power Distribution Systems with Internet of Thing for System Efficiency Enhancement

Optimisation of an aggregator’s customer portfolio based on inter-user compatibility

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