Energy Exchange Control in Multiple Microgrids with Transactive Energy Management

Daneshvar, Mohammadreza; Mohammadi‐Ivatloo, Behnam; Abapour, Mehdi; Asadi, Somayeh

doi:10.35833/mpce.2018.000590

Cited by 52 publications

(20 citation statements)

References 30 publications

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“…The output power of wind turbines has been described by () [24]:

P_{w t}^{t . s} (v^{t . s}) = \{\begin{matrix} 0 i f v^{t . s} \leq v_{i n}^{c} o r v^{t . s} \geq v_{o u t}^{c} \\ \frac{v^{t . s} - v_{i n}^{c}}{v_{r} - v_{i n}^{c}} P_{w}^{r} i f v_{i n}^{c} \leq v^{t . s} \leq v_{r} \\ P_{w}^{r} i f v_{r} \leq v^{t . s} \leq v_{o u t}^{c} \end{matrix},

where

v_{i n}^{c} . v_{r}

and

v_{o u t}^{c}

are cut‐in wind speed, rated wind speed, and cut‐out wind speed, respectively, and

P_{w}^{r}

is the rated output power of the wind turbine. Another formulation of wind turbines can be found in [27].…”

Section: Renewable Energy Resources and Uncertaintymentioning

confidence: 99%

“…where v c in .v r and v c out are cut-in wind speed, rated wind speed, and cut-out wind speed, respectively, and P r w is the rated out-put power of the wind turbine. Another formulation of wind turbines can be found in [27]. Also, Beta distribution is usually used to model the radiation probability distribution [28].…”

Section: Wind Turbine and Photovoltaic Generationmentioning

confidence: 99%

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Daily operation of multi‐energy systems based on stochastic optimization considering prediction of renewable energy generation

Azizi

Karimi

Jadid

2021

IET Renewable Power Gen

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Nowadays, the energy crisis is one of the most critical challenges facing countries. To deal with this crisis, instead of independently optimizing each of the energy carriers (electricity, gas, heat etc.), all carriers are considered simultaneously by a concept called multi‐energy system, which not only leads to economic benefits but also has environmental benefits. Here, a mixed‐integer linear programming model is proposed to minimize the total daily cost of a local multi‐energy system including the cost of energy exchange with the main grid, the cost of natural gas, and carbon emission costs. A polynomial neural network model is used to forecast the hourly wind speed and radiation of the next day. Also, a probabilistic scenario‐generation and scenario reduction method is utilized to generate the possible scenarios from the probability density function. The simulation results show that the proposed model using neural network prediction and stochastic optimization increases the total cost of the multi‐energy system by 12% in the worst‐case. Sensitivity analysis of loads, electricity prices, and gas prices have been used to investigate the behaviour of variables on energy hub operating costs.

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“…The output power of wind turbines has been described by () [24]:

P_{w t}^{t . s} (v^{t . s}) = \{\begin{matrix} 0 i f v^{t . s} \leq v_{i n}^{c} o r v^{t . s} \geq v_{o u t}^{c} \\ \frac{v^{t . s} - v_{i n}^{c}}{v_{r} - v_{i n}^{c}} P_{w}^{r} i f v_{i n}^{c} \leq v^{t . s} \leq v_{r} \\ P_{w}^{r} i f v_{r} \leq v^{t . s} \leq v_{o u t}^{c} \end{matrix},

where

v_{i n}^{c} . v_{r}

and

v_{o u t}^{c}

are cut‐in wind speed, rated wind speed, and cut‐out wind speed, respectively, and

P_{w}^{r}

is the rated output power of the wind turbine. Another formulation of wind turbines can be found in [27].…”

Section: Renewable Energy Resources and Uncertaintymentioning

confidence: 99%

Section: Wind Turbine and Photovoltaic Generationmentioning

confidence: 99%

Daily operation of multi‐energy systems based on stochastic optimization considering prediction of renewable energy generation

Azizi

Karimi

Jadid

2021

IET Renewable Power Gen

View full text Add to dashboard Cite

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“…In terms of energy management, a thorough discussion can be found in [48]- [51], which discuss and propose different energy management schemes for use in TE-SG systems. P2P schemes, which are based on distributed ledge technologies can be used in a decentralized/distributed architecture, whereas central trading schemes would typically suffice for the centralized architecture.…”

Section: Hierarchical Architecturementioning

confidence: 99%

Transactive Energy: State-of-the-Art in Control Strategies, Architectures, and Simulators

Onumanyi¹,

Isaac

Kruger

et al. 2021

IEEE Access

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The concept of transactive energy (TE) in smart grid systems is gaining increased research attention for its potential to optimize distributed energy resources, improve system reliability, as well as provide a balanced ecosystem for fair economic transaction between prosumers. TE is defined by the GridWise Architecture Council as a system of economic and control mechanisms that allows the dynamic balance of supply and demand across the entire electrical infrastructure using value as a key operational parameter. With control mechanisms being a key part of TE systems, in this article, we discuss the state-of-the-art in TE control strategies, architectures, and relevant simulators for designing, evaluating, and analysing TE systems. Most importantly, existing TE control strategies are examined and discussed via a hierarchical structure comprising four different levels wherein TE control strategies/controllers can be deployed. Architecture-wise, we highlight the different types of TE architectures including the centralized, decentralized, distributed, and hierarchical architecture. In terms of existing and potential simulators for designing and evaluating TE models, we discuss and compare notable software across different characteristics of interest. We conclude this article by highlighting the basic components of a typical TE controller and other future research directions spanning across security concerns, privacy issues, communication challenges, simulation and validation demands. As a main contribution, different from existing survey articles, this article presents a synthesis of existing works regarding TE control strategies, architectures, and TE-based simulators for the benefit of the budding researcher whose interest may lie in the study of TE systems.

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“…2. The wind and solar systems are used for cost-effective electricity supplying in each microgrid [23]. Moreover, DG units and BESU are operated for mitigating the uncontrollable features of stochastic generators [24].…”

Section: Problem Analysismentioning

confidence: 99%

A Novel Transactive Energy Model for Reliable Operation of Resilient Multi-Microgrids Cluster

Daneshvar

Mohammadi‐Ivatloo

Zare

et al. 2021

2021 IEEE International Conference on Environment and Electrical Engineering and 2021 IEEE Industrial and Commercial Power Syst

Self Cite

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Nowadays, as energy networks are mixed to form a hybrid structure, resiliency is found as a vital factor for the sustainability of the energy network infrastructure. In this regard, microgrids are recognized as a potential solution in postdisaster energy network recovery efforts. However, exposure to various natural disasters, as well as uncertainties associated with renewables, have introduced the resiliency challenge as a critical issue in the optimal operation of multi-microgrids. To tackle such challenge, this paper advances the state-of-art microgrid energy sharing technique by developing an innovative transactive energy trading model for the optimal resilient operation of the renewable-based multi-microgrids cluster. The proposed transactive energy-based model is intended to improve the resiliency of microgrids in the sustainable energy supply chain after affecting the system by natural disasters. Due to the operation of microgrids with a high contribution of renewable energy sources (RESs), uncertainty quantification is performed using autoregressive integrated moving average and fast forward selection methods to produce and reduce scenarios, respectively. The effectiveness and feasibility of the proposed model are examined using a modified IEEE-10 bus test system. The results indicate a 6.18% reduction in the operation cost of the multi-microgrids under the proposed transactive energy-based model.

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Energy Exchange Control in Multiple Microgrids with Transactive Energy Management

Cited by 52 publications

References 30 publications

Daily operation of multi‐energy systems based on stochastic optimization considering prediction of renewable energy generation

Daily operation of multi‐energy systems based on stochastic optimization considering prediction of renewable energy generation

Transactive Energy: State-of-the-Art in Control Strategies, Architectures, and Simulators

A Novel Transactive Energy Model for Reliable Operation of Resilient Multi-Microgrids Cluster

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