Intelligent Economic Operation of Smart-Grid Facilitating Fuzzy Advanced Quantum Evolutionary Method

Chakraborty, Shantanu; Ito, Takayuki; Senjyu, Tomonobu; Saber, Ahmed Yousuf

doi:10.1109/tste.2013.2256377

Cited by 55 publications

(25 citation statements)

References 31 publications

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“…The first case is provided for the base 10 TUs without including wind power generators to illustrate the effectiveness of the suggested BSA with respect to other well-known methods such as integer-coded GA (ICGA) [13], dynamic programming (DP) [20], Lagrangian relaxation (LR) [20], GA [20], self-adaptive bat-inspired algorithm (SABA) [23], and intelligent quantum inspired evolutionary algorithm (IQEA) [24]. The results are shown in Table I.…”

Section: -Case Study 1: Single Objective Uc Without Wind Powermentioning

confidence: 99%

A New Biobjective Probabilistic Risk-Based Wind-Thermal Unit Commitment Using Heuristic Techniques

Bavafa

Niknam

Azizipanah‐Abarghooee

et al. 2017

IEEE Trans. Ind. Inf.

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Abstract-Large penetration of wind generating units in power systems necessitates a flexible unit commitment tool to handle the intermittent nature of these units as well as demand. Moreover, power system operators face not only the risks of wind power curtailments, but also probable unit outages. Therefore, assessing a trade-off between operational costs and such risks is very important. In the proposed approach, the probability of the residual demand falling within the up-and-down spinning reserve imposed by n-1 security criterion is converted into a risk index. A new bi-objective probabilistic risk/cost based unit commitment model is proposed to simultaneously minimize both the operational costs and risk. The novel formulation presented provides a new power re-dispatch process to satisfy up-and-down ramp rate constraints. A new operational cycles based unit commitment algorithm is developed. The approach profits from a new non-dominated sorting backtracking search optimization algorithm for extracting the Pareto optimal set. The proposed approach is shown to provide superior results when applied to two test systems: a) 10-unit and b) IEEE 118-bus, 54-unit system.

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Section: -Case Study 1: Single Objective Uc Without Wind Powermentioning

confidence: 99%

A New Biobjective Probabilistic Risk-Based Wind-Thermal Unit Commitment Using Heuristic Techniques

Bavafa

Niknam

Azizipanah‐Abarghooee

et al. 2017

IEEE Trans. Ind. Inf.

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“…In addition, the scheduling dispatch of DGs in the MG is a particular issue in that it can be formulated as a non-linear and mixed-integer problem. Some techniques, such as Particle Swarm Optimisation (PSO), the Fuzzy Advanced Quantum Evolutionary Method (FAQA), the Chaotic Quantum Genetic Algorithm (CQGA), and the Artificial Bee Colony (ABC), etc., have been proposed to solve this problem and have shown their effectiveness [27][28][29][30][31]. The common drawback of these techniques is the lack of guarantee that the optimal solutions will always be located in a local optimum.…”

Section: Introductionmentioning

confidence: 99%

An Optimal Scheduling Dispatch of a Microgrid under Risk Assessment

Lin

Yang

et al. 2018

Energies

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This paper presents the scheduling dispatch of a microgrid (MG), while considering renewable energy, battery storage systems, and time-of-use price. For the risk evaluation of an MG, the Value-at-Risk (VAR) is calculated by using the Historical Simulation Method (HSM). By considering the various confidence levels of the VAR, a scheduling dispatch model of the MG is formulated to achieve a reasonable trade-off between the risk and cost. An Improved Bee Swarm Optimization (IBSO) is proposed to solve the scheduling dispatch model of the MG. In the IBSO procedure, the Sin-wave Weight Factor (SWF) and Forward-Backward Control Factor (FBCF) are embedded in the bee swarm of the BSO to improve the movement behaviors of each bee, specifically, its search efficiency and accuracy. The effectiveness of the IBSO is demonstrated via a real MG case and the results are compared with other methods. In either a grid-connected scenario or a stand-alone scenario, an optimal scheduling dispatch of MGs is carried out, herein, at various confidence levels of risk. The simulation results provide more information for handling uncertain environments when analyzing the VAR of MGs.

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“…Common optimization algorithms include clonal selection [22], particle swarm optimization [23], differential evolution (DE) [24], [25] and fuzzy advanced quantum evolution [26]. Since the EMS model of microgrids is complicated, optimization algorithms are likely to be trapped into local convergence during the process of iterations.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective energy management system for DC microgrids based on the maximum membership degree principle

Wang

Meng

et al. 2017

J. Mod. Power Syst. Clean Energy

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With the increasing quantity of DC electrical equipment, DC microgrids have been paid more and more attention. This paper proposes an approach to multi-objective optimisation of an energy management system (EMS) for a DC microgrid that includes a hybrid energy storage system (HESS). The operating and maintenance cost and the loss of power supply probability (LPSP) of the system are used as optimisation targets. The power flows of all distributed generators (DGs) in the DC microgrid during operating period are optimized. Based on the improved differential evolution (DE) algorithm, and by using the multi-objective non-dominated sorting method and the maximum membership degree principle (MMDP) of fuzzy control, the overall satisfaction degree of Pareto solutions to power flow optimization can be obtained. Simulation results verify the effectiveness of the proposed EMS optimization scheme, which is able to achieve an effective trade-off between the economy and the reliability of microgrid operation.

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Intelligent Economic Operation of Smart-Grid Facilitating Fuzzy Advanced Quantum Evolutionary Method

Cited by 55 publications

References 31 publications

A New Biobjective Probabilistic Risk-Based Wind-Thermal Unit Commitment Using Heuristic Techniques

A New Biobjective Probabilistic Risk-Based Wind-Thermal Unit Commitment Using Heuristic Techniques

An Optimal Scheduling Dispatch of a Microgrid under Risk Assessment

Multi-objective energy management system for DC microgrids based on the maximum membership degree principle

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