Integrating Electric Vehicles into Power System Operation Production Cost Models

The electrification of the transportation sector will increase the demand for electric power, potentially impacting the peak load and power system operations. A change such as this will be multifaceted. A power system production cost model (PCM) is a useful tool with which to analyze one of these facets, the operation of the power system. A PCM is a computer simulation that mimics power system operation, i.e., unit commitment, economic dispatch, reserves, etc. To understand how electric vehicles (EVs) will affect power system operation, it is necessary to create models that describe how EVs interact with power system operations that are suitable for use in a PCM. In this work, EV charging data from the EV Project, reported by the Idaho National Laboratory, were used to create scalable, statistical models of EV charging load profiles suitable for incorporation into a PCM. Models of EV loads were created for uncoordinated and coordinated charging. Uncoordinated charging load represents the load resulting from EV owners that charge at times of their choosing. To create an uncoordinated charging load profile, the parameters of importance are the number of vehicles, charger type, battery capacity, availability for charging, and battery beginning and ending states of charge. Coordinated charging is where EVs are charged via an “aggregator” that interacts with a power system operator to schedule EV charging at times that either minimize system operating costs, decrease EV charging costs, or both, while meeting the daily EV charging requirements subject to the EV owners’ charging constraints. Beta distributions were found to be the most appropriate distribution for statistically modeling the initial and final state of charge (SoC) of vehicles in an EV fleet. A Monte Carlo technique was implemented by sampling the charging parameters of importance to create an uncoordinated charging load time series. Coordinated charging was modeled as a controllable load within the PCM to represent the influence of the EV fleet on the system’s electricity price. The charging models were integrated as EV loads in a simple 5-bus system to demonstrate their usefulness. Polaris Systems Optimization’s PCM power system optimizer (PSO) was employed to show the effect of the EVs on one day of operation in the 5-bus power system, yielding interesting and valid results and showing the effectiveness of the charging models.

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Coordinated charging of electric vehicles from power system operation perspective

2014 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC) ◽

10.1109/appeec.2014.7066182 ◽

2014 ◽

Author(s):

Jianlong Guo ◽

Fushuan Wen ◽

Jiansheng Huang

Keyword(s):

Power System ◽

Electric Vehicles ◽

System Operation ◽

Power System Operation ◽

Coordinated Charging

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Power system operation risk analysis considering charging load self-management of plug-in hybrid electric vehicles

Applied Energy ◽

10.1016/j.apenergy.2014.09.069 ◽

2014 ◽

Vol 136 ◽

pp. 662-670 ◽

Cited By ~ 16

Author(s):

Zhe Liu ◽

Dan Wang ◽

Hongjie Jia ◽

Ned Djilali

Keyword(s):

Risk Analysis ◽

Power System ◽

Electric Vehicles ◽

Hybrid Electric Vehicles ◽

Self Management ◽

System Operation ◽

Power System Operation ◽

Hybrid Electric ◽

Operation Risk

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Improving Load Forecasting of Electric Vehicle Charging Stations Through Missing Data Imputation

Energies ◽

10.3390/en13184893 ◽

2020 ◽

Vol 13 (18) ◽

pp. 4893

Author(s):

Byungsung Lee ◽

Haesung Lee ◽

Hyun Ahn

Keyword(s):

Power System ◽

Load Forecasting ◽

Imputation Method ◽

Forecasting Model ◽

System Operation ◽

Power System Operation ◽

Forecasting Accuracy ◽

Electric Vehicle Charging ◽

Ev Charging ◽

Imputation Approach

As the penetration of electric vehicles (EVs) accelerates according to eco-friendly policies, the impact of electric vehicle charging demand on a power distribution network is becoming significant for reliable power system operation. In this regard, accurate power demand or load forecasting is of great help not only for unit commitment problem considering demand response but also for long-term power system operation and planning. In this paper, we present a forecasting model of EV charging station load based on long short-term memory (LSTM). Besides, to improve the forecasting accuracy, we devise an imputation method for handling missing values in EV charging data. For the verification of the forecasting model and our imputation approach, performance comparison with several imputation techniques is conducted. The experimental results show that our imputation approach achieves significant improvements in forecasting accuracy on data with a high missing rate. In particular, compared to a strategy without applying imputation, the proposed imputation method results in reduced forecasting errors of up to 9.8%.

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A transition from manual to Intelligent Automated power system operation -A Indicative Review

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v9i4.pp2274-2280 ◽

2019 ◽

Vol 9 (4) ◽

pp. 2274

Author(s):

Yamanappa N. Doddamani ◽

U. C. Kapale

Keyword(s):

Power System ◽

Energy Management ◽

Power Delivery ◽

Islanding Detection ◽

Energy Audit ◽

System Operation ◽

Power System Operation ◽

Power System Operations ◽

Detection Energy ◽

Manual Mode

<p>This paper reviews the transition of the power system operation from the traditional manual mode of power system operations to the level where automation using Internet of Things (IOT) and intelligence using Artificial Intelligence (AI) is implemented. To make the review paper brief only indicative papers are chosen to cover multiple power system operation based implementation. Care is taken there is lesser repeatation of similar technology or application be reviewed. The indicative review is to take only a representative literature to bypass scrutinizing multiple literatures with similar objectives and methods. A brief review of the slow transition from the traditional to the intelligent automated way of carrying out power system operations like the energy audit, load forecasting, fault detection, power quality control, smart grid technology, islanding detection, energy management etc is discussed .The Mechanical Engineering Perspective on the basis of applications would be noticed in the paper although the energy management and power delivery concepts are electrical.</p>

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Power system operation considering detailed modelling of energy storage systems

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v11i1.pp182-200 ◽

2021 ◽

Vol 11 (1) ◽

pp. 182

Author(s):

Sergio Cantillo ◽

Ricardo Moreno

Keyword(s):

Energy Storage ◽

Power Systems ◽

Power System ◽

Storage Systems ◽

Peak Load ◽

Energy Storage Systems ◽

System Operation ◽

Power System Operation ◽

Renewable Power ◽

And Storage

The power system operation considering energy storage systems (ESS) and renewable power represents a challenge. In a 24-hour economic dispatch, the generation resources are dispatched to meet demand requirements considering network restrictions. The uncertainty and unpredictability associated with renewable resources and storage systems represents challenges for power system operation due to operational and economical restrictions. This paper develops a detailed formulation to model energy storage systems (ESS) and renewable sources for power system operation considering 24-hour period. The model is formulated and evaluated with two different power systems (i.e. 5-bus and IEEE modified 24-bus systems). Wind availability patterns and scenarios are used to assess the ESS performance under different operational circumstances. With regard to the systems proposed, there are scenarios in order to evaluate ESS performance. In one of them, the increase in capacity did not represent significant savings or performance for the system, while in the other it was quite the opposite especially during peak load periods.

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