A non-linear resilient-oriented planning of the energy hub with integration of energy storage systems and flexible loads

Seyyedi, Abbas Zare Ghaleh; Armand, Mohammad Javad; Akbari, Ehsan; Moosanezhad, Jaber; Khorasani, Fatemeh; Raeisinia, Mohammadreza

doi:10.1016/j.est.2022.104397

Cited by 17 publications

(9 citation statements)

References 44 publications

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“…The optimal solution can be obtained by solving the MILP model using commercial solvers such as CPLEX. However, some of the other proposed methodologies in the literature such as References [14] and [15] use a non‐linear model that the optimal solution cannot be guaranteed.…”

Section: Case Studymentioning

confidence: 99%

“…Some research on multi‐carrier energy networks proposed a non‐linear single‐level form of the problem. Resilient‐oriented optimal management of a multi‐carrier energy system considering a non‐linear optimization approach associated with energy storage systems and flexible loads is reported in [14]. The results indicate that the operating cost of the multi‐carrier energy network during the outage condition for CHP is reduced by 63.48% through the implementation of the DRPs.…”

Section: Introductionmentioning

confidence: 99%

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Probabilistic/information gap decision theory‐based bilevel optimal management for multi‐carrier network by aggregating energy communities

Dorahaki

Rashidinejad

Ardestani

et al. 2023

IET Renewable Power Gen

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Energy communities are one of the vital puzzle pieces of future smart cities. This paper proposes a novel structure for a sustainable smart city by integrating energy communities in a multi‐carrier energy network. Each energy community has a manager; the so‐called Energy Community Managers (ECM), who trades energy with the upstream Multi‐Carrier Network Operator (MCNO). On the other hand, MCNO participates in the upstream energy markets to satisfy the demands of energy communities by maximizing its own profit. Therefore, ECMs and MCNO should solve a bilevel optimization problem associated with some common variables at both levels such as: energy carrier price and the amount of energy carrier exchange. In fact, MCNO is the leader and ECMs are the followers of such a bilevel optimization problem. Strong duality is employed to convert the bilevel optimization into a single level, while uncertainties are modelled by information gap decision theory and a scenario‐based approach. Sensitivity analysis shows that the thermal energy selling price and the gas buying price are the most crucial influencing on the profit of MCNO by 3.22% and −3.91%, respectively. Furthermore, the obtained results indicate that the risk attitude of the multi‐carrier energy network operator has a critical role in the total profit.

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Section: Case Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Probabilistic/information gap decision theory‐based bilevel optimal management for multi‐carrier network by aggregating energy communities

Dorahaki

Rashidinejad

Ardestani

et al. 2023

IET Renewable Power Gen

View full text Add to dashboard Cite

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“…Finally, in [15], expansion planning of electricity and gas networks is modelled by considering the resiliency factor. Table 1 shows the summary of recent studies [16,17]. Develop a model for planning of hub, taking into account various uncertainties, in which the sizing of hub converters is performed with the aim of improving hub resilience [18].…”

Section: Literature Reviewmentioning

confidence: 99%

“…It should be mentioned that this paper applies the Newton-Raphson method to find a power flow solution. Additionally, a penalty function is used for estimating the operation limits of the generation units ( 6)-(7), voltage angle limit (8), and series FACTS devices (16).…”

Section: Solution Processmentioning

confidence: 99%

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Co‐planning of generation and transmission expansion planning for network resiliency improvement against extreme weather conditions and uncertainty of resiliency sources

Seyyedi

Rashid

Akbari

et al. 2022

IET Generation Trans & Dist

Self Cite

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Generation and transmission expansion increase the flexibility of power systems and hence their ability to deal with contingency. This paper presents a resilient‐constrained generation and transmission expansion planning (RCGTEP) model considering the occurrence of earthquakes and floods. The proposed model minimizes the investment and operation costs of resiliency sources (RSs) and resiliency (blackout) costs arising from the outage of the network against the occurrence of extreme conditions. For further consideration, uncertainties of load and RSs availability are included as a Stochastic programming model. A hybrid solver of teaching‐learning‐based optimization (TLBO) and krill herd optimization (KHO) is used to solve the proposed problem and achieve the optimal solution, including a low standard deviation in the final optimal response. The model is tested using a modified version of the IEEE 6‐Bus and IEEE 89‐Bus transmission networks. Numerical results show the potential of the mentioned approach to improve indices of operation, economics, and resiliency in the transmission network.

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Robust $${H}_{\infty }$$ filtering and time-varying delay compensation for car suspension systems: a comprehensive approach to reduced disturbance and enhanced estimation

Liao

2024

Multiscale and Multidiscip. Model. Exp. and Des.

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A non-linear resilient-oriented planning of the energy hub with integration of energy storage systems and flexible loads

Cited by 17 publications

References 44 publications

Probabilistic/information gap decision theory‐based bilevel optimal management for multi‐carrier network by aggregating energy communities

Probabilistic/information gap decision theory‐based bilevel optimal management for multi‐carrier network by aggregating energy communities

Co‐planning of generation and transmission expansion planning for network resiliency improvement against extreme weather conditions and uncertainty of resiliency sources

Robust $${H}_{\infty }$$ filtering and time-varying delay compensation for car suspension systems: a comprehensive approach to reduced disturbance and enhanced estimation

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