Coordinated Electric Vehicle Charging With Reactive Power Support to Distribution Grids

Wang, Jing‐Yuan; Bharati, Guna R.; Paudyal, Sumit; Ceylan, Oğuzhan; Bhattarai, Bishnu P.; Myers, Kurt S.

doi:10.1109/tii.2018.2829710

Cited by 146 publications

(93 citation statements)

References 36 publications

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“…There is a rich literature on DER integration into Distribution Networks. Numerous studies address the impact of various DERs (primarily EVs and PVs) on the grid and its assets -e.g., overvoltages due to PV -and examine DER capabilities -e.g., the provision of reactive power [5]- [14]. Several works investigate also the impact of EVs on distribution transformers [6]- [8], [10], [11].…”

Section: A Background and Motivationmentioning

confidence: 99%

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Distribution Network Marginal Costs: Enhanced AC OPF Including Transformer Degradation

Andrianesis

Caramanis

2020

IEEE Trans. Smart Grid

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This two-part paper considers the day-ahead operational planning problem of a radial distribution network hosting Distributed Energy Resources (DERs) including Solar Photovoltaic (PV) and storage-like loads such as Electric Vehicles (EVs). We estimate dynamic Distribution nodal Location Marginal Costs (DLMCs) of real and reactive power and employ them to co-optimize distribution network and DER schedules. In Part I, we develop a novel AC Optimal Power Flow (OPF) model encompassing transformer degradation as a short-run network variable cost, and we decompose real/reactive power DLMCs into additive marginal cost components related to (i) the costs of real/reactive power transactions at the T&D interface/substation, (ii) real/reactive power marginal losses, (iii) voltage and (iv) ampacity congestion, and (v) a new transformer degradation marginal cost component. Our detailed transformer degradation model captures the impact of incremental transformer loading during a specific time period, not only on its Loss of Life (LoL) during that period, but also during subsequent time periods. To deal with this phenomenon, we develop methods that internalize the marginal LoL occurring beyond the daily horizon into the DLMCs evaluated within this horizon. In Part II, we use real distribution feeders to exemplify the use of DLMCs as financial incentives that convey sufficient information to optimize Distribution Network, and DER (PV and EV) operation.

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Section: A Background and Motivationmentioning

confidence: 99%

“…p e,t , ∀e, z = 1, ..., Z e ,(13)u e,τ beg z+1 = u e,τ end z − ∆u e,z , ∀e, z = 1, ..., Z e − 1,(14) u min e,t ≤ u e,t ≤ C B e , ∀e, t ∈ T end e ,…”

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Distribution Network Marginal Costs: Enhanced AC OPF Including Transformer Degradation

Andrianesis

Caramanis

2020

IEEE Trans. Smart Grid

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“…Constraints (5) and (6) indicate ramp down and up limits, whereas constraints (7) and (8) represent maximum/minimum active and reactive power generation. Constraints (9) to (14) represent AC security constraints in which Equations (9) and (10) include active and reactive power load balance, Equations (11) and (12) govern AC power flow that are nonlinear algebraic equations, bus voltage limits are shown in Equation (13), and Equation (14) indicates transmission flow limit.…”

Section: Problem Formulation Of Stochastic Scucmentioning

confidence: 99%

“…7,10 Further, an optimal electric vehicle charging model is considered to better utilizethe reactive power support for EV to grid operation and vice versa. 11 Fast stochastic SCUC scenario-based model with uncertain load and wind power generation is formulated and solved by interior point estimation method is presented in Mehrtash et al 12,13 Literature shows the impact of large-scale PEV integration on stochastic SCUC considering hourly load and wind power uncertainties. [14][15][16] The study shows that PEVs would not only reduce network but also traffic congestion, thereby reducing grid operational costs.…”

Section: Introductionmentioning

confidence: 99%

Optimal scheduling of electric vehicle in stochastic AC SCUC problem for large‐scale wind power penetration

Gupta

Jain

Sharma

et al. 2019

Int Trans Electr Energ Syst

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Summary Integration of highly volatile wind generation causes reliability and grid issues for system operator (SO). Plug‐in electric vehicles (PEVs) are mobile distributed source of active power that provides opportunity to use their battery storage for wind integration. The coordinated integration of wind volatility and PEVs fleet is studied under security‐constrained unit commitment (SCUC) model. In this regard, a stochastic SCUC with PEVs considering wind integration and line contingency is proposed. Wind volatility and PEVs driving behavior uncertainty is modeled through Monte Carlo simulations (MCS) of large number of scenarios with associated probabilities. This scenario has been reduced by Kantorovich distance (KD) matrix–based backward reduction technique. Moreover, pre‐line and post‐line contingency AC optimal power flow is used for network constraints in SCUC (AC SCUC). Due to consideration of N‐1 security criteria and wind power scenarios, the proposed model is mixed integer nonlinear programming (MINLP), which is computationally heavy and is thus solved by a two‐stage programming Benders decomposition (BD) approach. Different case studies are examined on modified IEEE reliability test system (RTS). Comparative analysis explores the impact on overall operational costs, PEV cost, wind curtailment, and locational marginal price (LMP) for congestion management. Simulation results validate that the proposed model is technoeconomically suitable for large‐scale wind power penetration.

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“…For example, Mojdehi and Ghosh proposed a framework to calculate the reactive power supply function of EVs for providing on‐demand reactive power service at the minimum cost . Wang et al developed a hierarchical coordination framework to optimally manage active and reactive powers dispatching EVs to benefit both the grid operations and EV charging . Farahani et al analyzed the plug‐in hybrid electric vehicle (PHEV) in the reactive power market and proposed the expected payment function of PHEV to minimize the total payment .…”

Section: Introductionmentioning

confidence: 99%

A Real‐Time Multilevel Energy Management Strategy for Electric Vehicle Charging in a Smart Electric Energy Distribution System

He³

et al. 2019

Energy Tech

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The randomness of electric vehicle (EV) charging has negative impacts on three‐phase imbalance and peak–valley difference in electric energy distribution systems. Traditional EV charging strategies have shortcomings: the performance of three‐phase imbalance mitigation may be limited if the grid‐connected EVs are extremely imbalanced on three phases; in addition, the comprehensive regulation of peak–valley difference and three‐phase imbalance is not developed, and the three‐phase imbalance of reactive power is ignored. Therefore, a real‐time multilevel energy management strategy (RMEMS) for EV charging is proposed. A tri‐level optimization model (TOM) is designed as the central system. In upper‐level optimization, the three‐phase selection (TPS) of EVs is optimized to balance active or reactive power consumption on three phases. Based on the results from upper‐level optimization, the charging active power is regulated in middle‐level optimization to reduce the peak–valley difference on each phase. In lower‐level optimization, the reactive power compensated by EV chargers is optimized based on the results from upper‐level and middle‐level optimization to balance the reactive power on three phases. Case studies show that the proposed RMEMS performs well for balancing active and reactive power consumption on three phases, and the peak–valley differences of active power consumption on each phase are all mitigated.

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Coordinated Electric Vehicle Charging With Reactive Power Support to Distribution Grids

Cited by 146 publications

References 36 publications

Distribution Network Marginal Costs: Enhanced AC OPF Including Transformer Degradation

Distribution Network Marginal Costs: Enhanced AC OPF Including Transformer Degradation

Optimal scheduling of electric vehicle in stochastic AC SCUC problem for large‐scale wind power penetration

A Real‐Time Multilevel Energy Management Strategy for Electric Vehicle Charging in a Smart Electric Energy Distribution System

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