Scheduling and Pricing of Load Flexibility in Power Systems

Khatami, Roohallah; Heidarifar, Majid; Parvania, Masood; Khargonekar, Pramod P.

doi:10.1109/jstsp.2018.2849861

Cited by 31 publications

(7 citation statements)

References 24 publications

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“…There is little doubt that the adoption of EV and residential solar systems will continue to increase. As such, utilities ought to prepare to upgrade existing transformers, increase maintenance routines or introduce novel strategies to control EV charging and benefit from its potential flexibility [27], [28], as the penetration of distributed energy resource increases. state or reflect those of the United States Government or any agency thereof.…”

Section: Discussionmentioning

confidence: 99%

Data-Driven Risk Analysis of Joint Electric Vehicle and Solar Operation in Distribution Networks

Palomino

Parvania

2020

IEEE Open J. Power Energy

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Increasing electric vehicle (EV) charging demand and residential solar photovoltaic (PV) generation greatly alter traditional distribution system operation and have the potential to overload and otherwise threaten the operating life of legacy infrastructure. The rate and location of adoption of these technologies on residential distribution systems introduce operational uncertainties for which traditional utilities may not be prepared. This paper proposes a user-defined, data-driven risk assessment method to quantify the severity and likelihood of transformer and secondary conductor overload conditions posed by high levels of EV charging demand coupled with rooftop solar PV. The stochasticity inherent in the operation of a distribution secondary system is captured by employing data-driven probability distribution functions for residential loading, EV charging, rooftop solar generation and ambient temperature. Samples then are repeatedly drawn from each function as inputs to a Monte-Carlo, multi-period power flow analysis to calculate secondary line currents, total loading and accelerated transformer aging. The proposed approach is utilized to study transformer and secondary conductor overload risk as well as transformer loss-of-life for multiple EV and PV penetration scenarios using historical EV charging profiles and residential Salt Lake City load profile data for a peak summer load day.

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Section: Discussionmentioning

confidence: 99%

Data-Driven Risk Analysis of Joint Electric Vehicle and Solar Operation in Distribution Networks

Palomino

Parvania

2020

IEEE Open J. Power Energy

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“…, is modelled as a piecewise linear function in (35) where C ODG is the minimum generation cost and f j is the incremental cost of power generation in segment j.…”

Section: Odgmentioning

confidence: 99%

“…The first term in second line of (6) is the energy production cost of the ODG, and the second term is the net‐cost of providing flexibility to the grid, which equals the flexible load rescheduling cost minus the incentive payment received from system operators. Here we assume the customers are paid for load reduction based to the energy market prices, which is consistent with the current FERC order 745 [34, 35].…”

Section: Proposed Stochastic Risk‐based Flexibility Scheduling Modelmentioning

confidence: 99%

Stochastic risk‐based flexibility scheduling for large customers with onsite solar generation

Angizeh

Parvania

2019

IET Renewable Power Generation

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This study proposes a two-stage stochastic optimisation model to co-optimise the onsite flexibility resources of large customers with the decisions to purchase power from bilateral contracts and forward energy market, while considering the uncertainty of onsite solar generation and market prices. In addition to onsite solar generation, the proposed model integrates the energy flexibility provided by the customers' flexible loads, onsite energy storage and onsite dispatchable generation. The uncertainty of onsite solar generation and market prices are characterised by autoregressive integrated moving average models, which are used to generate scenarios for the proposed stochastic optimisation model. The proposed two-stage stochastic optimisation model, formulated as a mixed-integer linear programming problem, minimises the expected energy procurement cost of the customers by optimising the first and second stage decisions. The first stage decisions include the utilisation of bilateral contracts, flexible loads and onsite energy storage and dispatchable generation, while decisions to purchase power from, and selling excess solar power to the energy market are optimised in the second stage. The proposed model integrates conditional value-at-risk as a risk measure that would enable the customers to manage the financial risks associated with the uncertainty of onsite solar generation and market prices. The proposed model is implemented on a test large industrial customer, where the advantages of the proposed model are investigated through multiple case studies.

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“…RTP is a method of dynamically adjusting electricity price via the relationship between the power system supply and demand [21]. In the power system, a fundamental model is proposed for continuous-time scheduling and marginal pricing of energy generation storage in day-ahead power systems' operation [22]. In the home energy management system (HEMS), a new charging and discharging strategy for EVs with energy price control is proposed for the best benefits of EV owners.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Optimization of Large‐Scale EVs Charging Path Planning and Charging Pricing Strategy for Charging Station

Hou

Luo

et al. 2021

Complexity

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With the increasing number of electric vehicles (EVs), the charging demand of EVs has brought many new research hotspots, i.e., charging path planning and charging pricing strategy of the charging stations. In this paper, an integrated framework is proposed for multiobjective EV path planning with varied charging pricing strategies, considering the driving distance, total time consumption, energy consumption, charging fee such factors, while the charging pricing strategy is designed based on the objectives of maximizing the total revenues of the charging stations and balancing the profits of the charging stations. First, the energy consumption model of EVs, the M/M/S queuing model of charging stations, and the charging model of charging piles are established. A novel charging path planning algorithm is proposed based on bidirectional Martins’ algorithm, which can assist EV users to select charging stations and plan charging paths. Then, a particle swarm optimization (PSO) algorithm is applied to solve the optimal solution of charging station pricing designation. Finally, the method proposed in the paper is simulated on the street map of Shenzhen to verify the efficacy of the multiobjective charging path planning for EVs and the feasibility of the charging pricing strategy.

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Scheduling and Pricing of Load Flexibility in Power Systems

Cited by 31 publications

References 24 publications

Data-Driven Risk Analysis of Joint Electric Vehicle and Solar Operation in Distribution Networks

Data-Driven Risk Analysis of Joint Electric Vehicle and Solar Operation in Distribution Networks

Stochastic risk‐based flexibility scheduling for large customers with onsite solar generation

Multiobjective Optimization of Large‐Scale EVs Charging Path Planning and Charging Pricing Strategy for Charging Station

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