scholarly journals A reliability-constrained demand response-based method to increase the hosting capacity of power systems to electric vehicles

2020 ◽  
Vol 121 ◽  
pp. 106046 ◽  
Author(s):  
MD Kamruzzaman ◽  
Mohammed Benidris
Keyword(s):  
Power Systems ◽  
Electric Vehicles ◽  
Demand Response

scholarly journals Economic Impacts of the Demand Response of Electric Vehicles Considering Battery Degradation

Energies ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 5771
Author(s):  
Yumiko Iwafune ◽  
Kazuhiko Ogimoto
Keyword(s):  
Power Systems ◽  
Electric Vehicles ◽  
Demand Response ◽  
Degradation Mechanism ◽  
Evaluation Model ◽  
Photovoltaic System ◽  
Driving Patterns ◽  
Battery Degradation ◽  
Reduction Of Co2 ◽  
The Impact

The increase in the number of electric vehicles (EVs) has led to increased global expectations that the application of this technology may result in the reduction of CO2 emissions through the replacement of conventional petrol vehicles and ensure the flexibility of power systems such as batteries. In this paper, we propose a residential demand response (DR) evaluation model that considers the degradation mechanism of the EV battery and examines the effective battery operation. We adopted the already-proposed NiMnCo battery degradation model to develop an EV DR evaluation model. In this model, the battery operation is optimized to minimize the electricity and degradation costs affected by ambient temperature, battery state of charge (SOC), and depth of discharge. In this study, we evaluated the impact of the relevant parameters on the economics of the DR of EV batteries for 10 all-electric detached houses with photovoltaic system assuming multiple EV driving patterns and battery (dis)charging constraints. The results indicated that the degradation costs are greatly affected by the SOC condition. If a low SOC can be managed with a DR strategy, the total cost can be reduced. This is because the sum of the reduction of purchased cost from the utility and calendar degradation costs are higher than the increase of the cycle degradation cost. In addition, an analysis was conducted considering different driving patterns. The results showed that the cost reduction was highest when a driving pattern was employed in which the mileage was low and the staying at home time was large. When degradation costs are included, the value of optimized charging and discharging operations is more apparent than when degradation costs are not considered.

scholarly journals Chance-Constrained Optimization Dispatch and Demand Response Control of Thermostatically Controlled Loads and Plug-in Electric Vehicles

2021 ◽  
Author(s):  
Jianqiang Hu ◽  
Jinde Cao
Keyword(s):  
Power Systems ◽  
Electric Vehicles ◽  
Demand Response ◽  
Wind Farm ◽  
Sample Average Approximation ◽  
Chance Constraint ◽  
Response Control ◽  
Renewable Power ◽  
Look Ahead ◽  
Thermostatically Controlled Loads

Demand response flexible loads can provide fast regulation and ancillary services as reserve capacity in power systems. This paper proposes a joint optimization dispatch control strategy for source-load system with stochastic renewable power injection and flexible thermostatically controlled loads (TCLs) and plug-in electric vehicles (PEVs). Specifically, the optimization model is characterized by a chance constraint look-ahead programming to maximal the social welfare of both units and load agents. By solving the chance constraint optimization with sample average approximation (SAA) method, the optimal power scheduling for units and TCL/PEV agents can be obtained. Secondly, two demand response control algorithms for TCLs and PEVs are proposed respectively based on the aggregate control models of the load agents. The TCLs are controlled by its temperature setpoints and PEVs are controlled by its charging power such that the DR control objective can be fulfilled. The effectiveness of the proposed dispatch and control algorithm has been demonstrated by the simulation studies on a modified IEEE 39 bus system with a wind farm, a photovoltaic power station, two TCL agents and two PEV agents.

Impact of plug-in hybrid electric vehicles on power systems with demand response and wind power

Energy Policy ◽  
2011 ◽  
Vol 39 (7) ◽  
pp. 4016-4021 ◽  
Author(s):  
Jianhui Wang ◽  
Cong Liu ◽  
Dan Ton ◽  
Yan Zhou ◽  
Jinho Kim ◽  
...  
Keyword(s):  
Power Systems ◽  
Wind Power ◽  
Electric Vehicles ◽  
Demand Response ◽  
Hybrid Electric Vehicles ◽  
Hybrid Electric

scholarly journals Research on ordered charge and discharge of cluster electric vehicle based on index selection

2019 ◽  
Vol 272 ◽  
pp. 01023
Author(s):  
Chunxuan Hu ◽  
Tianran Li ◽  
Chao Yuan
Keyword(s):  
Power Systems ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Demand Response ◽  
Control Strategy ◽  
Assessment Model ◽  
Cost Constraints ◽  
Basic Characteristics ◽  
The Cost ◽  
The Impact

The basic characteristics of electric vehicles are important basis for studying the behavior of electric vehicles. According to the basic characteristics of electric vehicles, this paper establishes an electric vehicle convergence model and its control strategy with demand-side response. Taking into account the demand for electric vehicles, electric vehicle aggregators and power companies, reducing the cost of control, while reducing the impact on electric vehicles. Based on the real-time state of charge, the conditions of electric vehicle in the network and other factors to build the assessment model of the scheduling potential, and then put forward the demand response indicators of electric vehicles, and give the corresponding aggregation strategy. considering the multiple constraints , such as the cost constraints of electric vehicles participating in grid regulation, the charging requirements of electric vehicle owners, and the battery consumption of electric vehicles, a control strategy model is proposed for electric vehicles participating in demand response of power systems. The simulation test shows that the aggregation strategy can not only meet the travel needs of electric vehicle owners, but also reduce the impact on the electric vehicle caused by frequent switching of charge and discharge status. In addition, it can also reduce the cost of grid regulation.

scholarly journals Online Learning Algorithms for the Real-Time Set-Point Tracking Problem

2021 ◽  
Vol 11 (14) ◽  
pp. 6620
Author(s):  
Arman Alahyari ◽  
David Pozo ◽  
Meisam Farrokhifar
Keyword(s):  
Decision Making ◽  
Power Systems ◽  
Real Time ◽  
Demand Response ◽  
Tracking Problem ◽  
Smart Meters ◽  
Set Point ◽  
Point Tracking ◽  
Power Set ◽  
Decision Making Framework

With the recent advent of technology within the smart grid, many conventional concepts of power systems have undergone drastic changes. Owing to technological developments, even small customers can monitor their energy consumption and schedule household applications with the utilization of smart meters and mobile devices. In this paper, we address the power set-point tracking problem for an aggregator that participates in a real-time ancillary program. Fast communication of data and control signal is possible, and the end-user side can exploit the provided signals through demand response programs benefiting both customers and the power grid. However, the existing optimization approaches rely on heavy computation and future parameter predictions, making them ineffective regarding real-time decision-making. As an alternative to the fixed control rules and offline optimization models, we propose the use of an online optimization decision-making framework for the power set-point tracking problem. For the introduced decision-making framework, two types of online algorithms are investigated with and without projections. The former is based on the standard online gradient descent (OGD) algorithm, while the latter is based on the Online Frank–Wolfe (OFW) algorithm. The results demonstrated that both algorithms could achieve sub-linear regret where the OGD approach reached approximately 2.4-times lower average losses. However, the OFW-based demand response algorithm performed up to twenty-nine percent faster when the number of loads increased for each round of optimization.

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