Power Conditioning of Distribution Networks via Single-Phase Electric Vehicles Equipped

Pirouzi, Sasan; Aghaei, Jamshid; Niknam, Taher; Khooban, Mohammad Hassan; Dragičević, Tomislav; Farahmand, Hossein; Korpås, Magnus; Blaabjerg, Frede

doi:10.1109/jsyst.2019.2896408

Cited by 46 publications

(44 citation statements)

References 29 publications

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“…Artificial intelligence is an appropriate candidate in solving many of the real-world electrical problems [55][56][57][58][59][60][61][62][63][64][65][66][67][68][69][70][71]. In this part, we have solved a problem of combined heat and power economic dispatch [6,71] using our proposed method and different optimization algorithms.…”

Section: Experimental Results: a Real-world Problemmentioning

confidence: 99%

An Adaptive Particle Swarm Optimization Algorithm for Unconstrained Optimization

et al. 2020

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Conventional optimization methods are not efficient enough to solve many of the naturally complicated optimization problems. Thus, inspired by nature, metaheuristic algorithms can be utilized as a new kind of problem solvers in solution to these types of optimization problems. In this paper, an optimization algorithm is proposed which is capable of finding the expected quality of different locations and also tuning its exploration-exploitation dilemma to the location of an individual. A novel particle swarm optimization algorithm is presented which implements the conditioning learning behavior so that the particles are led to perform a natural conditioning behavior on an unconditioned motive. In the problem space, particles are classified into several categories so that if a particle lies within a low diversity category, it would have a tendency to move towards its best personal experience. But, if the particle’s category is with high diversity, it would have the tendency to move towards the global optimum of that category. The idea of the birds’ sensitivity to its flying space is also utilized to increase the particles’ speed in undesired spaces in order to leave those spaces as soon as possible. However, in desirable spaces, the particles’ velocity is reduced to provide a situation in which the particles have more time to explore their environment. In the proposed algorithm, the birds’ instinctive behavior is implemented to construct an initial population randomly or chaotically. Experiments provided to compare the proposed algorithm with the state-of-the-art methods show that our optimization algorithm is one of the most efficient and appropriate ones to solve the static optimization problems.

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Section: Experimental Results: a Real-world Problemmentioning

confidence: 99%

An Adaptive Particle Swarm Optimization Algorithm for Unconstrained Optimization

et al. 2020

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“…This paper uses the first model due to its simple formulation based on (2)- (9). The CPF is presented in (2)- (6), which, respectively, refer to nodal active and reactive power balance, active and reactive power flowing in distribution lines, and voltage angle value in the slack bus. Also, constraints (7)-(9) present the current flow model at harmonic frequencies.…”

Section: Ohpf Model Of Adn (First Level)mentioning

confidence: 99%

“…While a unidirectional converter controls active power in the direction of network to converter (N2C) [3], a bidirectional converter can manage active and reactive powers in N2C and converter to network directions [4]. Moreover, the unidirectional converter generally injects current harmonics into the network [5], whereas the bidirectional converter can compensate the load harmonics [6]. Hence, the sources and storages with bidirectional converters can improve the network indices such as the voltage profile, power losses etc., and can also com-pensate harmonics in the ADN.…”

Section: Introductionmentioning

confidence: 99%

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Bi‐level power management strategy in harmonic‐polluted active distribution network including virtual power plants

Rohani

Abasi

Beigzadeh

et al. 2021

IET Renewable Power Gen

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In this paper, a bi‐level power management strategy is presented for an active distribution network (ADN) in the presence of virtual power plant (VPP). In the proposed strategy, VPP contains renewable energy source (RES), energy storage system (ESS) and electric vehicles (EVs) parking lot coordinated with VPP operator (VPPO), and there is a coordinated framework between VPPO and distribution system operator. The proposed power management strategy (PMS) is considered in the context of the bi‐level optimization problem. In the first level, the optimal harmonic power flow formulation of the ADN is considered by minimizing the summation of energy cost, voltage deviation function and voltage THD% as a normalized objective function. In the second level, the VPP is modeled according to the framework of day‐ahead energy and ancillary services markets. In the next step, the linear format of the proposed non‐linear original problem is obtained with an optimal solution and low calculation burden. Also, the Karush‐Kuhn‐Tucker condition is used to convert the proposed bi‐level PMS approach to a single‐level model, and the scenariobased stochastic programming models the uncertainty of load, energy price, RES power, and EVs demand. Finally, the proposed strategy is applied on the 33‐bus distribution network using GAMS software.

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“…In practice, EV is plugged into ADN through the electronic-based converter interface. Pirouzi et al [11] show that these devices can achieve reactive power management and control with little cost. Reactive power is very important for ADN operation.…”

Section: Introductionmentioning

confidence: 99%

Active and Reactive Power Collaborative Optimization for Active Distribution Networks Considering Bi-Directional V2G Behavior

Zhang

Yang

et al. 2021

Sustainability

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Due to their great potential for energy conservation and emission reduction, electric vehicles (EVs) have attracted the attention of governments around the world and become more popular. However, the high penetration rate of EVs has brought great challenges to the operation of the Active Distribution Network (ADN). On the other hand, EVs will be equipped with more intelligent chargers in the future, which supports the EVs’ high flexibility in both active and reactive power control. In this paper, a distributed optimization model of ADN is proposed by employing the collaborative active and reactive power control capability of EVs. Firstly, the preference of EV users is taken into account and the charging mode of EVs is divided into three categories: rated power charging, non-discharging, and flexible charging–discharging. Then, the reactive power compensation capacity of the plugged-in EV is deduced based on the circuit topology of the intelligent charger and the active–reactive power control model of the EV is established subsequently. Secondly, considering the operation constraints of ADN and the charging–discharging constraints of EVs over the operation planning horizon, the optimization objective of the model is proposed, which consists of two parts: “minimizing energy cost” and “improving voltage profile”. Finally, a distributed solution method is proposed based on the Alternating Direction Method of Multipliers (ADMM). The proposed model is implemented on a 33-bus ADN. The obtained results demonstrate that it is beneficial to achieve lower energy cost and increase the voltage profile of the ADN. In addition, the energy demand of EV batteries in their plugin intervals is met, and the demand preference of EV users is guaranteed.

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Power Conditioning of Distribution Networks via Single-Phase Electric Vehicles Equipped

Cited by 46 publications

References 29 publications

An Adaptive Particle Swarm Optimization Algorithm for Unconstrained Optimization

An Adaptive Particle Swarm Optimization Algorithm for Unconstrained Optimization

Bi‐level power management strategy in harmonic‐polluted active distribution network including virtual power plants

Active and Reactive Power Collaborative Optimization for Active Distribution Networks Considering Bi-Directional V2G Behavior

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