Optimal distribution feeder reconfiguration and generation scheduling for microgrid day-ahead operation in the presence of electric vehicles considering uncertainties

Sedighizadeh, Mostafa; Shaghaghi‐shahr, Gholamreza; Esmaili, Masoud; Aghamohammadi, Mohammad Reza

doi:10.1016/j.est.2018.11.009

Cited by 64 publications

(27 citation statements)

References 34 publications

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“…However, such an optimal policy could rarely be found due to the extreme computational cost required for problems of interest. For instance, tabular methods work by maintaining a table of values over the states, or state‐action pairs, and this is simply intractable for larger problems due to the curse of dimensionality 29 . A solution to this is to use a function to approximate these values, where the function consists of less parameter that needs to be learned than the original problem itself.…”

Section: Proposed Algorithmmentioning

confidence: 99%

“…Here, the authors try to make a scheduling for a short term for this microgrid and depending on the storage energy systems such as fuel cell and batteries. 28 While, in Ref. 29 the authors apply reconfiguration for IEEE 33 bus system feeders as well as the scheduling for the generators using GAME software considering all operational constraints.…”

Section: Introductionmentioning

confidence: 99%

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Retracted: Optimal peak shifting of a domestic load connected to utility grid using storage battery based on deep Q‐learning network

Al-Gablawy

2020

Int J Energy Res

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Summary Peak periods are a result of consumers generally using electricity at similar times and periods as each other, for example, turning lights on when returning home from work, or the widespread use of air conditioners during the summer. Without peak shifting, the grid's system operators are forced to use peaked plants to provide additional energy. This operation is incredibly expensive and harmful to the environment due to its high levels of carbon emissions. Battery storage system (BSS) has been proposed to allow purchasing the energy during off‐peak periods for later use, with the primary objective of realizing peak shifting occurred. Multi‐objective optimization with the reinforcement learning technique has been utilized in order to achieve the primary objective, reduce energy consumption, and minimize the consumers' utility bills. The results revealed that the reduction in energy consumption was more than 20%, the consumers' energy bills were minimized, as well as realizing perfect peak shifting. In addition, the strategy attempted to overcharge the battery with about 7% of the time, and promising methods to address this has been proposed as a direction for future research.

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Section: Proposed Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Retracted: Optimal peak shifting of a domestic load connected to utility grid using storage battery based on deep Q‐learning network

Al-Gablawy

2020

Int J Energy Res

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“…2 Recently, in order to improve the economic aspects of MG operation, the MGs use the dispatchable and nondispatchable distributed generation (DG) units as well as plug in hybrid electric vehicles (PHEVs). 3 Generation patterns of non-dispatchable DGs such as wind turbines (WTs) and photovoltaics (PVs) follow a stochastic behavior leading to uncertainties that should be taken into consideration in optimal generation scheduling (OGS) of MGs. 4 Moreover, demand side management (DSM) in MG operation is related to demand response (DR) programs.…”

Section: Motivation and Incitementmentioning

confidence: 99%

A new optimal operation framework for balanced microgrids considering reconfiguration and generation scheduling simultaneously

Sedighizadeh

Shaghaghi‐shahr

Aghamohammadi

et al. 2020

Int Trans Electr Energ Syst

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Summary In this article, the optimal distribution feeder reconfiguration (DFR) and optimal generation scheduling (OGS) are simultaneously taken into account to enhance operational flexibility and efficiency in microgrids (MGs). The proposed model considers different features of MGs, including distributed generation (DG) units, plug in hybrid electric vehicles (PHEVs), and demand response (DR) programs. Three DR programs are modeled including, load shifting, load curtailing, and time of use (TOU) programs. Uncertainties of renewable generations and loads are modeled using a computationally competitive approach. Both controlled and uncontrolled charging/discharging patterns of PHEVs are investigated. To enhance tractability, the proposed model is formulated as a mixed‐integer second‐order cone programing (MISOCP) problem to minimize operational cost subject to technical and financial constraints. To evaluate the performance of the proposed model, it is examined on the balanced 33‐bus test systems in different case studies of DFR and OGS. The optimization problem is solved using the GAMS software package. It is found that PHEVs and DR not only enhances MG operation flexibility, but they also reduce the total operation cost and smooth load profile.

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“…Gas turbine and hydropower units are started at peak electricity price. The operating cost of gas turbine (C gt MEG-S ) is shown in (28). C gas is the price of gas.…”

Section: B Mathematical Model Of Meg-s 1) Objective Functionmentioning

confidence: 99%

“…In order to solve the day-ahead scheduling problem of integrated energy network with identifying redundant gas network constraints, reference [27] develops a bound tightening strategy combines Weymouth equation relaxation and an optimality-based bounds tightening method. Mixed-Integer Second-Order Cone Programming (MISOCP) method is used by reference [28] to achieve microgrid dayahead operation. In [29] and [30], the operation of MG is optimized by using the hierarchical genetic algorithm and multi objective evolution algorithm respectively.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Optimal Operation Control of Micro Energy Grid Coupling With Electricity- Thermal -Gas Considering Prosumer Characteristics

Wang

Yuan

2020

IEEE Access

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Under the increasingly stringent environmental regulations, the installed capacity of distributed renewable energy is increasing rapidly. How to fully absorb renewable energy without affecting power grid security and ensuring power quality is the key problem to be solved. The rapid development of energy production, conversion and storage equipment of prosumer makes the operation of micro energy grid possible. Therefore, according to the energy characteristics of different prosumer, this paper divides the micro energy grid into two types: micro energy grid of electricity selling and micro energy grid of energy supplying. The constrained nonlinear optimization method is used to solve the optimization problem of this high-dimensional nonlinear system with time delay. In this paper, the comparative study of optimal operation control between the two kinds of micro energy grid is carried out. In order to deal with the uncertainty of distributed renewable energy output and load forecasting deviation, this paper proposes a real time adaptive dynamic optimization control strategy based on deep learning. The strategy uses deep learning technology to pretrain the action network, so as to learn the optimal operation behavior of micro energy grid. Finally, the online simulation of micro energy grid for consecutive days is used to verify the correctness and real-time performance of the algorithm. INDEX TERMS Micro energy grid, distributed renewable energy, adaptive dynamic optimization, deep learning.

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Optimal distribution feeder reconfiguration and generation scheduling for microgrid day-ahead operation in the presence of electric vehicles considering uncertainties

Cited by 64 publications

References 34 publications

Retracted: Optimal peak shifting of a domestic load connected to utility grid using storage battery based on deep Q‐learning network

Retracted: Optimal peak shifting of a domestic load connected to utility grid using storage battery based on deep Q‐learning network

A new optimal operation framework for balanced microgrids considering reconfiguration and generation scheduling simultaneously

Real-Time Optimal Operation Control of Micro Energy Grid Coupling With Electricity- Thermal -Gas Considering Prosumer Characteristics

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