Risk aversion energy management in the networked microgrids with presence of renewable generation using decentralised optimisation approach

Ghaemi, Sina; Salehi, Javad; Aghdam, Farid Hamzeh

doi:10.1049/iet-rpg.2018.5573

Cited by 31 publications

(23 citation statements)

References 47 publications

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“…Under the hypothesis that the power balance can be forecasted on a given horizon [0, T], with a certain degree of uncertainty, e t can be divided, respectively, into deterministic ê t and stochastic e¯t components e t = ê t + e¯t (14) where ê t ∈ ℝ M − 1 is a known sequence of values over the interval [0, T]; e¯t ∈ ℝ M − 1 is an unknown sequence of independent identically distributed zero-mean random vectors over the interval [0, T], with Ee¯t x′ n = 0, ∀n ≤ t.…”

Section: Mg Modelmentioning

confidence: 99%

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Stochastic constrained linear quadratic control in a network of smart microgrids

Bersani

Dagdougui

Ouammi

et al. 2020

IET Renewable Power Generation

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Challenges of microgrids (MGs) energy management have gained more relevance with the presence of uncertainties in power generation and local loads. These problems significantly increase when related to network of smart MGs (NSMG). To address these challenges, this study presents a stochastic constrained control problem for the optimal management of a cooperative NSMG with interconnections allowing power exchanges. In this model, each MG can exchange power locally among each other as well as with the main electric grid. The proposed control approach is based on a linear-quadratic Gaussian problem definition for the optimal control of power flows under quadratic constraints limiting the variability of the power exchange as well as of the stored energy in each MG. The developed framework is applied to a cooperative network of four smart MGs to test and validate its effectiveness and performance. The network is connected to the main electric grid allowing power exchanges. The results demonstrate that the role of energy storage systems is undoubtedly becoming more and more relevant in the context of reacting to the stochastic behaviour of the balance between produced and consumed powers in MGs.

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Section: Mg Modelmentioning

confidence: 99%

“…Liu et al [13] developed a novel transactive energy control mechanism to coordinate the day-ahead collaboration among networked MGs. Ghaemi et al [14] proposed a risk aversion energy management in networked MGs. Khalili et al [15] presented an optimal, reliable, and resilient construction of dynamic self-adequate multi-MGs.…”

Section: Introductionmentioning

confidence: 99%

Stochastic constrained linear quadratic control in a network of smart microgrids

Bersani

Dagdougui

Ouammi

et al. 2020

IET Renewable Power Generation

View full text Add to dashboard Cite

show abstract

“…The generation of the scenarios has been performed independently and the correlation among the stochastic variables has been neglected. In order to reduce the burden of calculations, fast forward selection as the scenario reduction method has been employed [32]. however, due to the capability of reconfiguration, the DN operator tries to find the best topology for the DN to reduce active power loss, enhancing voltage stability and improving reliability index.…”

Section: Problem Formulationmentioning

confidence: 99%

“…Voltage constraint is brought in Equation 28and finally current limits according to the status of the switches is shown in Equation (31). The configuration of the DN should remain radial which is guaranteed through Equation (32). This equation indicates that the number of the closed lines in any loop should be smaller than the total number of the lines in that specific loop.…”

Section: First Step: Mg Local Energy Managementmentioning

confidence: 99%

Reconfiguration-based hierarchical energy management in multimicrogrid systems considering power losses, reliability index, and voltage enhancement

Aghdam¹,

Kalantari²,

Ravadanegh³

2020

Turk J Elec Eng & Comp Sci

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This paper presents a reconfiguration-based hierarchical energy management for interconnected microgrids known as multimicrogrid system. The goal is to minimize the operational costs of different entities alongside with finding the best topology to minimize the active power loss, enhance reliability index, and improve the voltage level. The distribution network (DN) includes several dispatchable and undispatchable distributed energy resources, energy storage devices, and multiple microgrids (MGs). The first layer of the optimization process is executed by each MG operator and each MG performs local energy management. Each MG operator informs the DN operator about its optimal schedules. Finally, global energy management with reconfiguration in the network topology is done by the DN operator. The energy management problem is modeled as a mixed-integer nonlinear programming problem. Simulations on a modified IEEE 33-bus distribution test system with multiple MGs is performed in GAMS environment.

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“…First part of the problem in (30) which involves objective function of the investment part and its related constraints is called master problem. Moreover, objective function's operation part with operation constraints is called sub-problem.…”

Section: Integration Of Benders Decomposition Techniquementioning

confidence: 99%

Assessment of flexibility index integration into the expansion planning of clean power resources and energy storage systems in modern distribution network using benders decomposition

Ghaemi

Salehi

2019

IET Renewable Power Generation

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High penetration of renewable resources on modern distribution networks increases the net load variability in due to the uncertain nature of these resources. Hence, the system may suffer from high ramping problem, the ignorance of which leads to the power interruption. Controlling the net load variability to provide adequate ramping also increases the operation costs significantly. Therefore, a new solution should be implemented to handle this problem. The aim of this study is to incorporate the flexibility indices into the expansion planning of distributed energy resources in modern distribution networks in order to demonstrate how optimal arrangement of flexible resources results in reducing the operation cost while controlling the net load variability. To this end, the benders decomposition approach was applied to the proposed expansion planning to reduce the complexity of the problem to a level which can be solved. The uncertainty of clean resources, load consumption and electricity market price was considered in the proposed problem along with the limitations of the investment budget and carbon emission permits. The simulation process was carried out on the IEEE 69-bus test system. The results indicate that optimal placement of the flexible resources considering the flexibility constraints is profitable from both technical and economic perspectives. Cost exp inv total investment cost

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Risk aversion energy management in the networked microgrids with presence of renewable generation using decentralised optimisation approach

Cited by 31 publications

References 47 publications

Stochastic constrained linear quadratic control in a network of smart microgrids

Stochastic constrained linear quadratic control in a network of smart microgrids

Reconfiguration-based hierarchical energy management in multimicrogrid systems considering power losses, reliability index, and voltage enhancement

Assessment of flexibility index integration into the expansion planning of clean power resources and energy storage systems in modern distribution network using benders decomposition

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