Optimal siting and sizing of renewable energy sources and charging stations simultaneously based on Differential Evolution algorithm

Moradi, Mohammad H.; Abedini, Moein; Tousi, S. M. Reza; Hosseinian, S. Mahdi

doi:10.1016/j.ijepes.2015.06.029

Cited by 135 publications

(60 citation statements)

References 22 publications

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“…In [26], electric vehicles (EVs) are integrated with MG in the presence of RESs. The major focus is to reduce power losses and improve the stability of MG under the large-scale integration of EVs.…”

Section: Related Workmentioning

confidence: 99%

“…This decrease is due to the discharge of BSS when the peak occurs at the grid, as also shown in Figure 4. On the other hand, the maximum cost in one time interval remains the same under GA and TLBO because the RE and BSS are used in high price hours (i.e., [24][25][26][27], where all techniques have nearly zero cost of electricity from the grid. Figure 16 depicts the hourly cost of electricity obtained from the grid after using local RESs and BSS.…”

Section: Electricity Demandmentioning

confidence: 99%

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Demand Side Management in Nearly Zero Energy Buildings Using Heuristic Optimizations

et al. 2017

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Today's buildings are responsible for about 40% of total energy consumption and 30-40% of carbon emissions, which are key concerns for the sustainable development of any society. The excessive usage of grid energy raises sustainability issues in the face of global changes, such as climate change, population, economic growths, etc. Traditionally, the power systems that deliver this commodity are fuel operated and lead towards high carbon emissions and global warming. To overcome these issues, the recent concept of the nearly zero energy building (nZEB) has attracted numerous researchers and industry for the construction and management of the new generation buildings. In this regard, this paper proposes various demand side management (DSM) programs using the genetic algorithm (GA), teaching learning-based optimization (TLBO), the enhanced differential evolution (EDE) algorithm and the proposed enhanced differential teaching learning algorithm (EDTLA) to manage energy and comfort, while taking the human preferences into consideration. Power consumption patterns of shiftable home appliances are modified in response to the real-time price signal in order to get monetary benefits. To further improve the cost and user discomfort objectives along with reduced carbon emission, renewable energy sources (RESs) are also integrated into the microgrid (MG). The proposed model is implemented in a smart residential complex of multiple homes under a real-time pricing environment. We figure out two feasible regions: one for electricity cost and the other for user discomfort. The proposed model aims to deal with the stochastic nature of RESs while introducing the battery storage system (BSS). The main objectives of this paper include: (1) integration of RESs; (2) minimization of the electricity bill (cost) and discomfort; and (3) minimizing the peak to average ratio (PAR) and carbon emission. Additionally, we also analyze the tradeoff between two conflicting objectives, like electricity cost and user discomfort. Simulation results validate both the implemented and proposed techniques.

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Section: Related Workmentioning

confidence: 99%

Section: Electricity Demandmentioning

confidence: 99%

Demand Side Management in Nearly Zero Energy Buildings Using Heuristic Optimizations

et al. 2017

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“…x a x (20)     (22) where   F  is the probability taken with respect to the  , given that it follows the probability distribution F , and 1   is the confidence level (  reflects the risk tolerance) which can be used to control the risk. By replacing (17) with (22), a DR joint chance constrained programming model for risk-averse joint capacity evaluation of PV generation and EVCSs is realized.…”

Section: Formulation Of Distributionally Roubust Joint Constrainedmentioning

confidence: 99%

“…Concerning the planning and assessment methods for EVCSs, reference [17] formulates a robust optimization planning methodological framework with the constraints of the power system and the transportation for sustainable integration of plug-in hybrid EVs into a power system; Wang et al [18] develop a multi-objective EVCS planning method which can ensure charging service while reducing energy losses and voltage fluctuations of distribution networks; reference [9] provides a comprehensive approach for evaluating the impact of different levels of EV penetration on distribution network investment and incremental energy losses. As for the research considering both DG and EVCSs, authors of [19] propose a multi-year multi-objective planning algorithm for enabling distribution networks to accommodate high penetrations of EVs in conjunction with DG; in [20], a method is developed to obtain the optimal siting and sizing of EVCSs and DG; reference [21] presents an analytical approach to determine the size of EVCSs powered by grid-connected PV penetration; a two-stage approach for allocation of EVCSs and DG in distribution networks is proposed in [22] considering both the economic benefits and network constraints.…”

Section: Introductionmentioning

confidence: 99%

Risk-averse joint capacity evaluation of PV generation and electric vehicle charging stations in distribution networks

Chen

Jia

et al. 2017

2017 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe)

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Increasing penetration of distribution generation (DG) and electric vehicles (EVs) calls for an effective way to estimate the achievable capacity connected to the distribution systems, but the exogenous uncertainties of DG outputs and EV charging loads make it challengeable. This study provides a joint capacity evaluation method with a risk threshold setting function for photovoltaic (PV) generation and EV charging stations (EVCSs). The method is mathematically formulated as a distributionally robust joint chance constrained programming model. And the worst-case conditional value at risk (WC-CVaR) approximation and an iterative algorithm based on semidefinite program (SDP) are used to solve the model. Finally, the method test is carried out numerically on IEEE 33-bus radial distribution system.

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“…Results validate the effectiveness of this model in minimizing the network operating costs and modifying the network demand curve. Reference [21] provides an algorithm for simultaneous determination of the optimal capacity of the charge stations and RESs, in which the EV charging pattern and the output of RESs are coordinated taking into account the peak demand factor at low-load hours and time-of-use electricity prices. In [22], a simultaneous optimization method is proposed to integrate RESs and EVs optimally into power systems.…”

Section: Introductionmentioning

confidence: 99%

A Bi-Layer Multi-Objective Techno-Economical Optimization Model for Optimal Integration of Distributed Energy Resources into Smart/Micro Grids

et al. 2020

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The energy management system is executed in microgrids for optimal integration of distributed energy resources (DERs) into the power distribution grids. To this end, various strategies have been more focused on cost reduction, whereas effectively both economic and technical indices/factors have to be considered simultaneously. Therefore, in this paper, a two-layer optimization model is proposed to minimize the operation costs, voltage fluctuations, and power losses of smart microgrids. In the outer-layer, the size and capacity of DERs including renewable energy sources (RES), electric vehicles (EV) charging stations and energy storage systems (ESS), are obtained simultaneously. The inner-layer corresponds to the scheduled operation of EVs and ESSs using an integrated coordination model (ICM). The ICM is a fuzzy interface that has been adopted to address the multi-objectivity of the cost function developed based on hourly demand response, state of charges of EVs and ESS, and electricity price. Demand response is implemented in the ICM to investigate the effect of time-of-use electricity prices on optimal energy management. To solve the optimization problem and load-flow equations, hybrid genetic algorithm (GA)-particle swarm optimization (PSO) and backward-forward sweep algorithms are deployed, respectively. One-day simulation results confirm that the proposed model can reduce the power loss, voltage fluctuations and electricity supply cost by 51%, 40.77%, and 55.21%, respectively, which can considerably improve power system stability and energy efficiency.Energies 2020, 13, 1706 2 of 25 microgrids is evolving quickly [1]. The advanced structure of the microgrids allows the distribution network operators (DSOs) to consider the EV battery, either as a load or distributed generator [2]. However, deployment of RESs and EVs creates some challenges, e.g., poor reliability and power quality problems. One of the most effective ways to mitigate these challenges is the deployment of energy storage systems (ESSs) in distribution grids. ESSs can alleviate the negative effects of uncertainties in RESs production [3]. On the other hand, despite the remarkable advantages of these units, there are some challenges in terms of adequate DERs capacity requirement, system configuration, and energy management and control. One of the most challenging issues is the optimal coordination of both supply and demand sides in microgrids with the main grid, while satisfying system constraints.Currently, extensive studies have been done on the impacts of the RESs, EVs, and ESSs on the network operation . In [4], a smart home energy management strategy is proposed to realize cost-effective energy systems for customers and provide reactive power compensation for home appliances using EV and ESS. In this paper, network-level studies are limited to investigating the power factor indicator, while the potential role of EVs and ESSs in distribution networks has not been considered. Researchers in [5] presented the centralized control strategy to manag...

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Optimal siting and sizing of renewable energy sources and charging stations simultaneously based on Differential Evolution algorithm

Cited by 135 publications

References 22 publications

Demand Side Management in Nearly Zero Energy Buildings Using Heuristic Optimizations

Demand Side Management in Nearly Zero Energy Buildings Using Heuristic Optimizations

Risk-averse joint capacity evaluation of PV generation and electric vehicle charging stations in distribution networks

A Bi-Layer Multi-Objective Techno-Economical Optimization Model for Optimal Integration of Distributed Energy Resources into Smart/Micro Grids

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