Coordinated Stochastic Scheduling for Improving Wind Power Adsorption in Electric Vehicles-Wind Integrated Power Systems by Multi-objective Optimization Approach

Li, Yuanzheng; Ni, Zhixian; Zhao, Tianyang; Yu, Minghui; Liu, Yun; Wu, Lei

doi:10.1109/ias.2019.8912450

Cited by 6 publications

(6 citation statements)

References 25 publications

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“…Therefore, up to now, many kinds of research have been conducted: In [14], impacts of the joint operation of WT power producers with EVs and other RLs have perused as a virtual power plant in electricity markets. In [15], EVs-WTs coordinated scheduling is applied to improve uncertain wind power adsorption. A profit-based unit commitment of TUs with renewable energy and EVs has been presented in [16].…”

Section: Introductionmentioning

confidence: 99%

Reliability-Based Thermal and Wind Units Economic Dispatch in Presence of DSRP

Arefi

Meyar‐Naimi

Shamim

2023

Preprint

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Recently, Wind Turbines (WTs) and Electric Vehicles (EVs) have been integrated into the demand side of many countries. WTs and EVs have uncertainties in electrical energy generation and consumption, respectively. Additionally, Thermal Units (TUs) suffer from random failures. As always, secure power system operation is the main goal of an independent system operator, therefore, these uncertainties should be considered. This paper proposes a two-stage reliability-based model for the economic dispatch of TUs and WTs in the presence of a demand-side response program. At the first stage, the well-being analysis is performed to determine the power generation and spinning reserve of the TUs regarding the timely power generation of WTs. At the second stage, the adoption of the responsive load consumption with the various conditions of the generation system in the power pool market is established using the cost of expected energy not served criterion. This optimization problem is solved at two stages using the genetic algorithm. To validate the proposed model, numerical studies have been applied to the generation part of an IEEE test power system including eleven TUs, one WT, and one thousand EVs.

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Section: Introductionmentioning

confidence: 99%

Reliability-Based Thermal and Wind Units Economic Dispatch in Presence of DSRP

Arefi

Meyar‐Naimi

Shamim

2023

Preprint

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“…In References 18 and 21 to maximize simultaneously the operational profits of distribution company operator (DISCO) and private owner of EVs and other energy resources, stochastic operational planning is proposed which coordinates EVs and rescheduling of energy sources. In paper 22, different uncertainties such as wind turbine output, EVs SOC, energy demand, and price are modeled to EVCS allocation. However, in Reference 22, uncertainties about various SOC levels of EVs and charging demand for EVs are not considered.…”

Section: Introductionmentioning

confidence: 99%

“…In paper 22, different uncertainties such as wind turbine output, EVs SOC, energy demand, and price are modeled to EVCS allocation. However, in Reference 22, uncertainties about various SOC levels of EVs and charging demand for EVs are not considered. This means that the quality of the results depend on the historical data accuracy for EVs behavior patterns.…”

Section: Introductionmentioning

confidence: 99%

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Optimal resiliency‐oriented charging station allocation for electric vehicles considering uncertainties

Akhgarzarandy

Wang

Farzinfar

2021

Int Trans Electr Energ Syst

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Summary This paper presents a resiliency‐oriented multi‐objective approach for optimal allocation of electric vehicles' (EV) charging station (EVCS) within distribution network. In addition, the effect of implementation of the demand response programs (DRP) is considered while allocating EVCSs. For this purpose, an appropriate modeling for considering various uncertainties such as consumption load, energy price, and initial state of EV charge are firstly presented. Moreover, the uncertainty and dynamic risk of wind speed during extreme weather conditions, as an important parameter that affects lines outage and grid resiliency, are accurately modeled. In the proposed methodology of this paper, the benefits obtained by ENS reduction and power loss is optimally shared with the EVCS owner to incentivize the investors to cooperate with DISCO and to invest in the construction of EVCS. The formulated problem is solved by utilization of an NSGA‐II‐based method embedded by scenario‐based approach and Monte‐Carlo method. The proposed formulation is applied to a standard network (IEEE 9 buses), and related Pareto‐front solutions for an economic‐resilient EVCS allocation, while maximizing benefits of Distribution Company Operator (DISCO) and EVCS owner are calculated. The results show that, in case of allocation two EVCSs and applying DRP program, total profits of DISCO is increased by 36.70% and 40.07%, respectively, compared with traditional grid that there is no EVCS or DRP implementation. Moreover, applying DRP, with its positive effect on peak load and power losses, reduces the EVCS capacities.

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“…In [12], a multiobjective optimization method based on sensitivity is proposed to optimal configuration of thyristor-controlled series capacitors in the transmission network, which can reduce the total reactive power loss and reduce the load capacity of the transmission line. In reference [13], a coordinated stochastic scheduling model based on the multiobjective optimization method is proposed to improve the absorption capacity of wind power on the premise of energy saving and emission reduction. e application of deep learning and reinforcement learning in multiobjective optimization provides a new idea for decision-making optimization of power equipment maintenance plan.…”

Section: Introductionmentioning

confidence: 99%

Optimization Method of Power Equipment Maintenance Plan Decision-Making Based on Deep Reinforcement Learning

Yang

Yao

2021

Mathematical Problems in Engineering

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The safe and reliable operation of power grid equipment is the basis for ensuring the safe operation of the power system. At present, the traditional periodical maintenance has exposed the abuses such as deficient maintenance and excess maintenance. Based on a multiagent deep reinforcement learning decision-making optimization algorithm, a method for decision-making and optimization of power grid equipment maintenance plans is proposed. In this paper, an optimization model of power grid equipment maintenance plan that takes into account the reliability and economics of power grid operation is constructed with maintenance constraints and power grid safety constraints as its constraints. The deep distributed recurrent Q-networks multiagent deep reinforcement learning is adopted to solve the optimization model. The deep distributed recurrent Q-networks multiagent deep reinforcement learning uses the high-dimensional feature extraction capabilities of deep learning and decision-making capabilities of reinforcement learning to solve the multiobjective decision-making problem of power grid maintenance planning. Through case analysis, the comparative results show that the proposed algorithm has better optimization and decision-making ability, as well as lower maintenance cost. Accordingly, the algorithm can realize the optimal decision of power grid equipment maintenance plan. The expected value of power shortage and maintenance cost obtained by the proposed method is $71.75$ $MW·H$ and $496000$ $yuan$.

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Coordinated Stochastic Scheduling for Improving Wind Power Adsorption in Electric Vehicles-Wind Integrated Power Systems by Multi-objective Optimization Approach

Cited by 6 publications

References 25 publications

Reliability-Based Thermal and Wind Units Economic Dispatch in Presence of DSRP

Reliability-Based Thermal and Wind Units Economic Dispatch in Presence of DSRP

Optimal resiliency‐oriented charging station allocation for electric vehicles considering uncertainties

Optimization Method of Power Equipment Maintenance Plan Decision-Making Based on Deep Reinforcement Learning

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