Optimal Distribution Grid Operation Using DLMP-Based Pricing for Electric Vehicle Charging Infrastructure in a Smart City

Canizes, Bruno; Soares, João; Vale, Zita; Corchado, Juan M.

doi:10.3390/en12040686

Cited by 41 publications

(20 citation statements)

References 82 publications

(82 reference statements)

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“…As described above, assessment of infrastructure sufficiency [5][6][7][8][9][10] and certain impacts in isolation, such as GHG emissions, energy impacts and air quality, have been previously reported in the literature [11][12][13][14][15][16][17][18][19][20][21][22][23][24]. Our paper builds upon these earlier developed approaches and expands them further.…”

Section: Ev Impactsmentioning

confidence: 83%

“…Besides studying the potential role of EVs in the smart grid, [14] highlights the role that EVs can play as a voltage source in off-grid systems, or as an uninterruptible power supply in cases of grid power failures. In their study of a locational marginal pricing model, [15] concludes that dynamic energy pricing for the charging of electric vehicles can decrease costs considerably both for EV users as well as distribution system operators. Ruiz-Rodriguez et al [16] and Hernández et al [17] studied the interaction of photovoltaic generation and EVs in radial distribution systems.…”

Section: Ev and Grid/market Interactionmentioning

confidence: 99%

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Assessing the Impacts of Electric Vehicle Recharging Infrastructure Deployment Efforts in the European Union

Thiel¹,

Julea²,

Iborra³

et al. 2019

Energies

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Electric vehicles (EVs) can play an important role in improving the European Union’s (EU)’s energy supply security, reducing the environmental impact of transport, and increasing EU competitiveness. The EU aims at fostering the synchronised deployment of EVs and necessary recharging infrastructure. There is currently a lack of studies in the literature for analysing the societal impacts of EV and infrastructure deployment at continental scale. In our paper, we analyse the likely impact of related plans of the EU member states (MSs). With the help of qualitative and quantitative analyses, we study the impact of plans on recharging infrastructure deployment, contributions to the EU climate and energy goals, air quality objectives, and reinforcement of the EU’s competitiveness and job creation. We soft-link a fleet impact model with a simplified source receptor relationship model, and propose a new model to calculate job impacts. The results overall show modest impacts by 2020, as most member states’ plans are not very ambitious. According to our analysis of the plans, a reduction of CO2 emissions by 0.4%, NOx emissions by 0.37%, and PM2.5 emissions by 0.44%, as well as a gross job creation of more than 8000 jobs will be achieved by 2020. The member state plans are very divergent. For countries with more ambitious targets up to 2020, such as Austria, France, Germany, and Luxemburg, the climate, energy, and air quality impacts are significant and show what would be achievable if the EU would increase its pace of EV and infrastructure deployment. We conclude that more ambitious efforts by the member states’ to deploy electric vehicles could accelerate the reduction of CO2 emissions and lead to less dependence on fossil oil-based fuels, along with air quality improvements, while at the same time creating new job opportunities in Europe. In regards to the ratio of publicly accessible recharging points (RPs) per EV, we conclude that member states have to come up with more ambitious targets for recharging point deployment, as the current plans will lead to only one recharging point per every 20 EVs by 2020 across the EU. This paper can serve as useful input to the further the planning of EV and recharging infrastructure deployment in the EU and elsewhere. Our study highlights that the different strategies that are followed in the EU member states can be a fertile ground to identify best practices. It remains a challenge to quantify how different support policies impact EV deployment. In terms of further research needs, we identify that more detailed studies are required to determine an appropriate level of infrastructure deployment, including fast chargers.

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Section: Ev Impactsmentioning

confidence: 83%

Section: Ev and Grid/market Interactionmentioning

confidence: 99%

Assessing the Impacts of Electric Vehicle Recharging Infrastructure Deployment Efforts in the European Union

Thiel¹,

Julea²,

Iborra³

et al. 2019

Energies

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“…The optimization problem in (19) aims to maximize the market surplus subject to constraints. DC approximation of the line flow is modeled using (20), while ( 21) and ( 22) denote the node balance, and variable load constraints, respectively. Eq ( 14) is modified as (23) to model angle difference approximation, and ( 24) and ( 25) represent analogous line segment length restrictions in (15) and (16).…”

Section: ) Lossy Dcopfmentioning

confidence: 99%

Optimization of Economic Efficiency in Distribution Grids Using Distribution Locational Marginal Pricing

2021

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Distribution locational marginal pricing (DLMP) is an increasingly popular pricing signal that can be used to incentivize grid-friendly behavior of distributed energy resources (DER) to optimize economic efficiency in distribution grids. In this paper, a lossy direct-current optimal power flow (DCOPF) is utilized to obtain the iterative calculation framework for DLMPs. The two-stage algorithm iterates between the transmission system optimal power flow (OPF) and the distribution system OPF until no significant changes in DLMPs are observed. Real power losses are estimated using a static piecewise linear approximation technique. A sampling algorithm is proposed to minimize the possible convergence issues associated with the proposed mathematical model. DLMPs are calculated for the i) contemporary, ii) enhanced, and iii) meshed distribution grids using an IEEE 34-bus test feeder. The test transmission system is modeled using an IEEE 30-bus system. Finally, the calculated DLMPs in the enhanced grid are compared against three existing pricing mechanisms via three case studies to prove its validity and superiority, especially in congested systems with high penetration of price-responsive loads (PRLs).

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“…In this research work, the DLMPs (which will permit to determine the variable charging price (see Equation (4))) are defined through Lagrangian multipliers of the corresponding constraints (power balance) of the optimisation problem which has the goal to minimise the DSO expenditures [34]. Thus, the DSO seeks to: The DLMPs optimization problem is classified as mixed-integer nonlinear programming (MINLP) due to the non-linearity features.…”

Section: Dlmp Optimisation Model Descriptionmentioning

confidence: 99%

“…To carry out the case studies a physical model of SC by GECAD-BISITE [34] was used. The considered SC presents five types of loads, namely: The schematic of the SC is presented in Figure 4 and the coordinates of each building can be seen in Table 3).…”

Section: Case Studiesmentioning

confidence: 99%

Electric Vehicles’ User Charging Behaviour Simulator for a Smart City

et al. 2019

Self Cite

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The increase of variable renewable energy generation has brought several new challenges to power and energy systems. Solutions based on storage systems and consumption flexibility are being proposed to balance the variability from generation sources that depend directly on environmental conditions. The widespread use of electric vehicles is seen as a resource that includes both distributed storage capabilities and the potential for consumption (charging) flexibility. However, to take advantage of the full potential of electric vehicles’ flexibility, it is essential that proper incentives are provided and that the management is performed with the variation of generation. This paper presents a research study on the impact of the variation of the electricity prices on the behavior of electric vehicle’s users. This study compared the benefits when using the variable and fixed charging prices. The variable prices are determined based on the calculation of distribution locational marginal pricing, which are recalculated and adapted continuously accordingly to the users’ trips and behavior. A travel simulation tool was developed for simulating real environments taking into account the behavior of real users. Results show that variable-rate of electricity prices demonstrate to be more advantageous to the users, enabling them to reduce charging costs while contributing to the required flexibility for the system.

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Optimal Distribution Grid Operation Using DLMP-Based Pricing for Electric Vehicle Charging Infrastructure in a Smart City

Cited by 41 publications

References 82 publications

Assessing the Impacts of Electric Vehicle Recharging Infrastructure Deployment Efforts in the European Union

Assessing the Impacts of Electric Vehicle Recharging Infrastructure Deployment Efforts in the European Union

Optimization of Economic Efficiency in Distribution Grids Using Distribution Locational Marginal Pricing

Electric Vehicles’ User Charging Behaviour Simulator for a Smart City

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