A hierarchical scheduling framework for resilience enhancement of decentralized renewable-based microgrids considering proactive actions and mobile units

Mansouri, Seyed Amir; Nematbakhsh, Emad; Ahmarinejad, Amir; Jordehi, Ahmad Rezaee; Javadi, Mohammad Sadegh; Marzband, Mousa

doi:10.1016/j.rser.2022.112854

Cited by 105 publications

(22 citation statements)

References 43 publications

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“…A large body of literature employs deep reinforcement learning algorithms with perceptual prediction and automatic decision-making capabilities. These algorithms rely on massive historical data and iterative trial-and-error learning to achieve autonomous management and collaborative training [36][37][38][39][40]. Reference [36] proposes a novel data-driven energy scheduling model by using the Convolutional Neural Networks (CNN) to improve the computational efficiency and map the pricing patterns to potential energy scheduling decisions of a prosumer, under various uncertain scenarios.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Reference [39] designed the CNN to extract both topological patterns of the power flows and uncertain patterns of RESs from high-dimensional scenarios. For data security and computing efficiency, reference [40] reduced energy not served (ENS) of critical consumers through data sharing among decentralized microgrids (DCMGs) and reduced computational burden by dividing the optimization problem into a three-stage model.…”

Section: Literature Reviewmentioning

confidence: 99%

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A collaborative training approach for multi energy systems in low‐carbon parks accounting for response characteristics

Bao,

Sun,

Zheng

2023

IET Renewable Power Gen

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Existing researches lack systematic design of the operation mechanism and overall implementation process of park oriented multi‐state energy system; The existing researches on multi‐state energy systems ignored the dynamic response characteristics of load and the coupling effect of units considering the impact of carbon emissions, and did not go deep into the collaborative management of multiple multi state energy system parks. To this end, this paper first proposes a two‐stage multi‐energy system operation architecture for low carbon parks, and establishes a mathematical model considering carbon emissions, operating costs and other objectives. The response space of load is optimized by considering the dynamic interaction characteristics of load response and the coupling characteristics of unit operation. Then, considering the problems of data security and training efficiency, F‐DDPG technology is used to conduct collaborative training for multiple campus systems. Finally, an example is given to prove the effectiveness of the method, which is not only conducive to reducing the operating cost of the system and the carbon emissions on the supply side, but also improves the convergence of model training. In the case of collaborative training in multiple parks, the method in this paper converges faster and has better economic benefits.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

A collaborative training approach for multi energy systems in low‐carbon parks accounting for response characteristics

Bao,

Sun,

Zheng

2023

IET Renewable Power Gen

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“…The available solar power, wind power, and state of charge values can be computed using the mathematical model proposed in [35, 38, 40]. The mechanical power proposed in [29] for the wind turbines is computed using, an Equation ().

\begin{equation}{P}_W = \frac{1}{2}{C}_p(\alpha ,\beta )\rho {\rm{V}}_w^3{\rm{A}}\end{equation}

where

{P}_W

the mechanical power of the wind turbine that is dependent on the speed of the wind that blows here ‘ρ’ is the air density (kg/m 3 ),

{V}_w

is the wind velocity, A is swept the area,

{C}_{p\ }

is the power coefficient.…”

Section: Microgrid Mode Selection Controllermentioning

confidence: 99%

“…The available solar power, wind power, and state of charge values can be computed using the mathematical model proposed in [35,38,40]. The mechanical power proposed in [29] for the wind turbines is computed using, an Equation (10).…”

Section: Microgrid Mode Selection Controllermentioning

confidence: 99%

“…The uncertainties are addressed using the stochastic formulation that shows improved results. In [29], the resilience of DC microgrids is addressed using 3 stage hierarchical approach whereas in [30] the a two‐stage stochastic model is used to address the severe uncertainties of distributed generations and their impact on the overall system. In [31], a multi objective model is developed for the energy hub for converters’ variable efficiency, equipment deprivation yearly expansion of the load and energy prices.…”

Section: Introductionmentioning

confidence: 99%

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A novel automated demand response control using fuzzy logic for islanded battery‐operated rural microgrids

Azeem

Memon

Baig

et al. 2023

IET Renewable Power Gen

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Islanded rural microgrid require continuous resource monitoring. Demand response schemes have been phenomenal in managing loads. However, urban demand response schemes are well equipped with market prices and peak time penalties to control deferrable loads. In rural microrids, regular loads such as fans, lights and water pumps are normally used that do not fall under category of deferrable loads. In addition, full liberty of utilizing regular load at any time, lack of awareness and no information of storage reserves make task of load management more complex. In this research fully automated two layered demand response scheme is designed for regular operating loads. The first layer control is load mode control. The mode of operation is decided on the state of charge (SoC) of battery. In second layer, fuzzy controller is designed on the consumer's routines, SoC and ambient temperature as membership function. Results are assessed in terms of consumers comfort and availablity of SoC. The load operation in automated demand response remained indentical to actual rutine operation as per consumer's desire with 5 to 7% deviation. In all modes of operation SoC levels remained 15% higher and heavy load operated 13.5% more compare to relevant study.

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Optimal Solar‐Biomass‐Diesel‐Generator Hybrid Energy for Water Pumping System Considering Demand Response

Amusan,

Nwulu,

Gbadamosi

2024

Engineering Reports

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The water pumping system is a vital daily operation for a sustainable society. The demand for this essential commodity is progressively growing around the world. Incorporating renewable energy sources (RES) into its operation mitigates adverse environmental effects stemming from greenhouse gas (GHG) emissions and reduces associated costs linked to fossil fuel energy sources. Hence, the optimal sizing of a hybrid mix of RES and non‐RES energy sources to power water pumping is essential. This study employs a generalized reduced gradient (GRG) optimization method to ascertain the most effective energy mix for water pumping system (WPS) application by integrating time of use demand response. This is achieved through four scenarios: Scenario 1 (S1) utilizes grid power and Diesel generator (DG) energy sources, Scenario 2 (S2) incorporates grid power, DG, and solar PV, Scenario 3 (S3) integrates grid power, DG, solar PV, and biomass energy sources, while Scenario 4 (S4) incorporates Time of Use (TOU) with the three aforementioned energy sources. The key findings reveal an ideal solution comprising of a mix of renewable PV and biomass along with non‐renewable grid energy source embedded with Time of Use demand response (S4) with optimal energy capacities of 4007 kW of PV, 4228 kW of grid, 234 kW of biomass, and 1085 of DG owing to the least cost energy and cost of water pumped of 0.75$/kW and 1.74$/kL, respectively, most volume of water pumped of 270.50 kL. These findings prove that the integration TOU demand response program with a hybrid mix of grid‐biomass‐DG‐solar PV is best suited for water pumping applications as it gives a cost‐competitive value.

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A hierarchical scheduling framework for resilience enhancement of decentralized renewable-based microgrids considering proactive actions and mobile units

Cited by 105 publications

References 43 publications

A collaborative training approach for multi energy systems in low‐carbon parks accounting for response characteristics

A collaborative training approach for multi energy systems in low‐carbon parks accounting for response characteristics

A novel automated demand response control using fuzzy logic for islanded battery‐operated rural microgrids

Optimal Solar‐Biomass‐Diesel‐Generator Hybrid Energy for Water Pumping System Considering Demand Response

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