Dependent Discrete Convolution Based Probabilistic Load Flow for the Active Distribution System

Wang, Yi; Zhang, Ning; Chen, Qixin; Yang, Jingwei; Chen, Kang; Huang, Junhui

doi:10.1109/tste.2016.2640340

Cited by 78 publications

(27 citation statements)

References 29 publications

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“…Fig. 4, we can see that based on the statistical characteristics of WT, PV and load outputs, the SOT is capable of dealing with the superposition of multiple uncertainties, which further confirms the conclusions of the work in reference [36].…”

Section: Simulation Scenarios 521 Outputs Of Dgs and Loadsupporting

confidence: 81%

“…SOT is a powerful mathematical tool for handling the uncertainties of power systems, which is based on the sequence convolution in the field of digital signal processing [36]. According to this theory, the probability distributions of random variables are denoted by probabilistic sequences, and a new sequence could be derived from the operations between sequences.…”

Section: Serialization Description Of Random Variables 411 Introducmentioning

confidence: 99%

“…According to this theory, the probability distributions of random variables are denoted by probabilistic sequences, and a new sequence could be derived from the operations between sequences. And then, the new probability distributions of random variables are obtained through mutual operations [36]. Specifically, four discrete sequence operations named addition-type-convolution (ATC) and subtraction-type-convolution (STC), AND-typeproduct and OR-type-product have been defined.…”

Section: Serialization Description Of Random Variables 411 Introducmentioning

confidence: 99%

See 2 more Smart Citations

Optimal scheduling of isolated microgrid with an electric vehicle battery swapping station in multi-stakeholder scenarios: A bi-level programming approach via real-time pricing

et al. 2018

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Section: Simulation Scenarios 521 Outputs Of Dgs and Loadsupporting

confidence: 81%

Section: Serialization Description Of Random Variables 411 Introducmentioning

confidence: 99%

Section: Serialization Description Of Random Variables 411 Introducmentioning

confidence: 99%

See 1 more Smart Citation

Optimal scheduling of isolated microgrid with an electric vehicle battery swapping station in multi-stakeholder scenarios: A bi-level programming approach via real-time pricing

et al. 2018

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“…3 shows that the original load and the joint output of renewable generations obtained via the ATC operation between WT and PV power outputs. By doing so, the uncertainties of multiple renewable generations are effectively tackled via the SOT [34]. Based on the results of the national household travel survey in 2009 made by the Federal Highway Administration of the Department of Transportation of the United States, an EV arrival case following the normal distribution in an entire scheduling period is randomly taken as an example in this study.…”

Section: B Basic Datamentioning

confidence: 99%

Incorporating Demand Response of Electric Vehicles in Scheduling of Isolated Microgrids With Renewables Using a Bi-Level Programming Approach

2019

IEEE Access

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In this work, a novel optimal scheduling approach is proposed for isolated microgrids (MGs) with renewable generations by incorporating demand response of electric vehicles (EVs). First, a bi-level programming-based MG scheduling model is proposed under real-time pricing environments, where the upper-and lower-levels seek to minimize the MG net operating cost and the EV charging cost. Second, a hybrid solution algorithm called JAYA-interior point method is put forward to solve the model. And finally, the simulation results demonstrate that incorporating demand response of electric vehicles is able to guide EV users to actively participate in MG scheduling and achieve the peak load shaving, which offers a fundamental way to balance the interests between MG and EV users.INDEX TERMS Microgrid, optimal scheduling, demand response, electric vehicles, renewable generations, bi-level programming

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“…In [13], the adjustable direct current OPF is presented and the objective function is taken as the total generation cost of units. And at the same time, the power flow characteristics of a modern power system are becoming increasingly complex due to the growing penetration of distributed generations [14][15][16][17] and the deployments of novel power electronic loads [18][19][20][21][22]. In this context, multi-objective OPF (MOPF) has received the extensive attention of researchers in the field of OPF [23][24][25][26][27][28], since it can coordinate different-weight or even conflicting multiple objectives.…”

Section: Introductionmentioning

confidence: 99%

Two-Step Many-Objective Optimal Power Flow Based on Knee Point-Driven Evolutionary Algorithm

2018

Processes

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To coordinate the economy, security and environment protection in the power system operation, a two-step many-objective optimal power flow (MaOPF) solution method is proposed. In step 1, it is the first time that knee point-driven evolutionary algorithm (KnEA) is introduced to address the MaOPF problem, and thereby the Pareto-optimal solutions can be obtained. In step 2, an integrated decision analysis technique is utilized to provide decision makers with decision supports by combining fuzzy c-means (FCM) clustering and grey relational projection (GRP) method together. In this way, the best compromise solutions (BCSs) that represent decision makers' different, even conflicting, preferences can be automatically determined from the set of Paretooptimal solutions. The primary contribution of the proposal is the innovative application of manyobjective optimization together with decision analysis for addressing MaOPF problems. Through examining the two-step method via the IEEE 118-bus system and the real-world Hebei provincial power system, it is verified that our approach is suitable for addressing the MaOPF problem of power systems.Keywords: optimal power flow; optimal operation; power systems; multi-objective optimization; knee point-driven evolutionary algorithm; decision analysis; best compromise solutions Recent research suggests that multi-objective evolutionary algorithms (MOEAs) are promising tools for addressing various challenging optimization tasks in engineering fields [29][30][31][32]. In order to optimal distributed generation planning, a MOEA is employed in [29]. Reference [30] reviews the most representative MOEAs that have been reported, and MOEA has developed as an effective method to solve such an optimization problem. In [31], MOEA is employed for planning overtime of software engineers. The layout of wind farms is optimized via MOEA in [32]. In particular, MOEAs can be also applied to solve the OPF issue [23][24][25][26][27][28]. Unfortunately, the MOPF can only cope with the optimization issue with two to three objectives, which, to a certain extent, limits the practicality of this type of methods. In addition, many-objective optimization problems (MaOPs), considering four or more objective functions in the OPF problem [33][34][35][36], are commonly existed phenomenon in the practice of real-world power system operation [33]. In [34], a specially tailored MOEA is presented for tackling the current large-scale MaOPs. Another MOEA based on adaptive search strategy is presented for coping with MaOPs in [35]. In [36], six different evolutionary algorithms (EAs) are tested, and the results prove that MOEAs exhibit their own capabilities in dealing with different MaOPs. For this reason, MaOPs have recently gained a great deal of attention as most existing MOEAs are inadequate for solving OPF problems with four or more objectives, and it has become a hotspot to enhance the ability of MOEAs for addressing MaOPs issues [37][38][39]. However, manyobjective OPF (MaOPF) is quite challenging for solving sinc...

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Dependent Discrete Convolution Based Probabilistic Load Flow for the Active Distribution System

Cited by 78 publications

References 29 publications

Optimal scheduling of isolated microgrid with an electric vehicle battery swapping station in multi-stakeholder scenarios: A bi-level programming approach via real-time pricing

Optimal scheduling of isolated microgrid with an electric vehicle battery swapping station in multi-stakeholder scenarios: A bi-level programming approach via real-time pricing

Incorporating Demand Response of Electric Vehicles in Scheduling of Isolated Microgrids With Renewables Using a Bi-Level Programming Approach

Two-Step Many-Objective Optimal Power Flow Based on Knee Point-Driven Evolutionary Algorithm

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