scholarly journals Transactive Energy for Aggregated Electric Vehicles to Reduce System Peak Load Considering Network Constraints

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 31519-31529 ◽  
Author(s):  
Arsalan Masood ◽  
Junjie Hu ◽  
Ai Xin ◽  
Ahmed Rabee Sayed ◽  
Guangya Yang
Keyword(s):  
Electric Vehicles ◽  
Peak Load ◽  
Network Constraints ◽  
Transactive Energy

scholarly journals QoE-Aware Smart Home Energy Management Considering Renewables and Electric Vehicles

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2304 ◽  
Author(s):  
Mingfu Li ◽  
Guan-Yi Li ◽  
Hou-Ren Chen ◽  
Cheng-Wei Jiang
Keyword(s):  
Energy Management ◽  
Electric Vehicles ◽  
Smart Home ◽  
Control Algorithm ◽  
Renewable Energy Sources ◽  
Peak Load ◽  
Electricity Bill ◽  
Home Energy Management ◽  
Smart Appliance ◽  
User Comfort

To reduce the peak load and electricity bill while preserving the user comfort, a quality of experience (QoE)-aware smart appliance control algorithm for the smart home energy management system (sHEMS) with renewable energy sources (RES) and electric vehicles (EV) was proposed. The proposed algorithm decreases the peak load and electricity bill by deferring starting times of delay-tolerant appliances from peak to off-peak hours, controlling the temperature setting of heating, ventilation, and air conditioning (HVAC), and properly scheduling the discharging and charging periods of an EV. In this paper, the user comfort is evaluated by means of QoE functions. To preserve the user’s QoE, the delay of the starting time of a home appliance and the temperature setting of HVAC are constrained by a QoE threshold. Additionally, to solve the trade-off problem between the peak load/electricity bill reduction and user’s QoE, a fuzzy logic controller for dynamically adjusting the QoE threshold to optimize the user’s QoE was also designed. Simulation results demonstrate that the proposed smart appliance control algorithm with a fuzzy-controlled QoE threshold significantly reduces the peak load and electricity bill while optimally preserving the user’s QoE. Compared with the baseline case, the proposed scheme reduces the electricity bill by 65% under the scenario with RES and EV. Additionally, compared with the method of optimal scheduling of appliances in the literature, the proposed scheme achieves much better peak load reduction performance and user’s QoE.

scholarly journals Research on the Performance of Electric Vehicles in Peak Load Shifting

2014 ◽  
Vol 2014 ◽  
pp. 1-7
Author(s):  
Qingshan Xu ◽  
Yujun Liu ◽  
Maosheng Ding ◽  
Pingliang Zeng ◽  
Wei Pan
Keyword(s):  
Potential Energy ◽  
Electric Vehicles ◽  
Peak Load ◽  
Load Shifting ◽  
Vehicle To Grid ◽  
Capacity Level ◽  
Simulation Results ◽  
The Relationship

Electric vehicles (EVs) are developing remarkably fast these years which makes the technology of vehicle-to-grid (V2G) easier to implement. Peak load shifting (PLS) is an important part of V2G service. A model of EVs’ capacity in V2G service is proposed for the research on PLS in this paper. The capacity is valued in accordance with three types of situations. Based on the model, three different scenarios are suggested in order to evaluate the capacity with MATLAB. The evaluation results indicate that EVs can provide potential energy to participate in PLS. Then, the principle of PLS with EVs is researched through the analysis of the relationship between their power and capacity. The performance of EVs in PLS is also simulated. The comparison of two simulation results shows that EVs can fulfill the request of PLS without intensely lowering their capacity level.

Peak-Load Management in Commercial Systems With Electric Vehicles

2019 ◽  
Vol 13 (2) ◽  
pp. 1872-1882 ◽  
Author(s):  
Khizir Mahmud ◽  
M. J. Hossain ◽  
Jayashri Ravishankar
Keyword(s):  
Electric Vehicles ◽  
Peak Load ◽  
Load Management

scholarly journals A Novel Algorithm for Controlling Active and Reactive Power Flows of Electric Vehicles in Buildings and Its Impact on the Distribution Network

2020 ◽  
Vol 11 (2) ◽  
pp. 43 ◽  
Author(s):  
Claude Ziad El-Bayeh ◽  
Khaled Alzaareer ◽  
Brahim Brahmi ◽  
Mohamed Zellagui
Keyword(s):  
Electric Vehicles ◽  
Reactive Power ◽  
Optimization Algorithms ◽  
Peak Load ◽  
Active Power ◽  
Economic Losses ◽  
Voltage Profile ◽  
Electricity Cost ◽  
Active And Reactive Power ◽  
Electricity Bill

In the literature, many optimization algorithms were developed to control electrical loads, especially Electric Vehicles (EVs) in buildings. Despite the success of the existing algorithms in improving the power profile of charging EVs and reducing the total electricity bill of the end-users, these algorithms didn’t show significant contribution in improving the voltage profile on the network, especially with the existence of highly inductive loads. The control of the active power may not be sufficient to regulate the voltage, even if sophisticated optimization algorithms and control strategies are used. To fill the gap in the literature, we propose a new algorithm that is able to control both the active and reactive power flows using electric vehicles in buildings and homes. The algorithm is composed of two parts; the first part uses optimization to control the active power and minimize the electricity bill, while the second part controls the reactive power using the bidirectional converter in the EV in a way that the voltage profile on the distribution transformer respects its limits. The new approach is validated through a comparative study of four different scenarios, (i) without EV, (ii) with EV using uncoordinated charging, (iii) with EV using coordinated charging, (iv) with EV using our proposed algorithm. Results show that our algorithm has maintained the voltage within the recommended limits, and it has minimized the peak load, the electricity cost, and the techno-economic losses on the network.

A Model Predictive Control-Based Energy Management Strategy Considering Electric Vehicle Battery Thermal and Cabin Climate Control

2020 ◽  
Author(s):  
Yuanzhi Liu ◽  
Jie Zhang
Keyword(s):  
Model Predictive Control ◽  
Energy Management ◽  
Predictive Control ◽  
Electric Vehicles ◽  
Management Strategy ◽  
Critical Role ◽  
Peak Load ◽  
Climate Control ◽  
Energy Management Strategy ◽  
Battery Thermal Management System

Abstract The energy management strategy plays a critical role in scheduling the operations and enhancing the overall efficiency for electric vehicles. This paper proposes an effective model predictive control-based (MPC) energy management strategy to simultaneously control the battery thermal management system (BTMS) and the cabin air conditioning (AC) system for electric vehicles (EVs). We aim to improve the overall energy efficiency, while retaining soft constraints from both BTMS and AC systems. It is implemented by optimizing the operation and discharging schedule to avoid peak load and by directly utilizing the regenerative power instead of recharging. Compared to the systematic performance without any control coordination between BTMS and AC, results reveal that there are a 4.3% reduction for the recharging energy, and a 6.5% improvement for the overall energy consumption that gained from the MPC-based energy management strategy. Overall the MPC-based energy management is a promising solution to enhance the efficiency for electric vehicles.

scholarly journals Battery Life Enhancement in a Hybrid Electrical Energy Storage System Using a Multi-Source Inverter

2019 ◽  
Vol 10 (2) ◽  
pp. 17 ◽  
Author(s):  
Yogesh Mahadik ◽  
K. Vadirajacharya
Keyword(s):  
Electric Vehicles ◽  
Storage System ◽  
Peak Load ◽  
Electrical Energy ◽  
Energy Storage System ◽  
Open Loop ◽  
Battery Life ◽  
Loop Control ◽  
Power Exchange ◽  
Driving Range

This paper introduces a new topology using a multi-source inverter with the intention of reducing the battery current and weight, while enhancing the battery life and increasing the driving range for plug-in electric vehicles, with the combination of a battery and an ultracapacitor (UC) as storage devices. The proposed topology interconnects the UC and battery directly to the three-phase load with a single-stage conversion using an inverter. The battery life is considerably reduced due to excess (peak) current drawn by the load, and these peak load current requirements are met by connecting the ultracapacitor to the battery, controlled through an inverter. Here, the battery is used to cater to the needs of constant profile energy demands, and the UC is used to meet the dynamic peak load profile. This system is highly efficient and cost-effective when compared to a contemporary system with a single power source. Through a comparative analysis, the cost-effectiveness of the proposed energy management system (EMS) is explained in this paper. Energy and power exchange are implemented with an open-loop control strategy using the PSIM simulation environment, and the system is developed with a hardware prototype using different modes of inverter control, which reduces the average battery current to 27% compared to the conventional case. The driving range of electric vehicles is extended using active power exchange between load and the sources. The dynamics of the ultracapacitor gives a quick response, with battery current shared by the ultracapacitor. As a result, the battery current is reduced, thereby enhancing the driving cycle. With the prototype, the results of the proposed topology are validated.

Electric Load Forecasting of Electric Vehicles in Charging

2014 ◽  
Vol 672-674 ◽  
pp. 1165-1168
Author(s):  
Wei Liu ◽  
Tao Wei ◽  
Ming Xin Zhao ◽  
Dan Xu ◽  
Chao Gao
Keyword(s):  
Electric Vehicles ◽  
Peak Load ◽  
Impact Factors ◽  
Electric Load ◽  
Load Curve ◽  
Total Load ◽  
Electricity Grid ◽  
Electric Cars ◽  
Market Management ◽  
The Impact

This paper forecast the electric load of the mass electric cars connected to the electric grid in charging and discharging; considered the inventory forecast of electric vehicles; comprehensive analyzed the charge and discharge characteristics of the electric cars’ charging infrastructures and the impact factors such as users’ behaviors as well as the using frequency, which lead to different load distribution at different times. It calculated the total load of electric vehicles into the load curve and the load curve of the characteristics under different regions (industrial, commercial and residential). Concludes that the mass electric cars connected to the electricity grid will increase the peak load of power grid, and lay the foundation for the subsequent market management and optimization control.

Research of Electric Vehicles' Discharging Price

2014 ◽  
Vol 543-547 ◽  
pp. 452-456 ◽  
Author(s):  
Ya Qi Ni ◽  
Jia Shi Yang ◽  
Dian Gang Wang ◽  
Cheng Wei Li ◽  
Jun Yong Liu ◽  
...  
Keyword(s):  
Electric Vehicles ◽  
Bilevel Programming ◽  
Driving Force ◽  
Peak Load ◽  
Minimum Cost ◽  
Bilevel Programming Problem ◽  
Load Shifting ◽  
Control Scheme ◽  
Relationship Of ◽  
The Relationship

Through the vehicle to the network (V2G) control, the objective of the "load shifting" is possible. Reasonable charging and discharging price is the fundamental driving force for EV users to participate in V2G and the analysis of the game process of the grid company and the EV users becomes critical. A bilevel programming problem is established which makes company the largest gains and consumers the minimum cost. Through a control scheme whose purpose is minimizing the sample variance of load, the relationship of annual discharging electricity energy and peak load decrement is obtained. At last the optimal discharging price of power grid company and the corresponding proportion of EV participating in V2G is calculated by chaos algorithm. It provides a reference for company to formulate discharging price.

scholarly journals Research on joint optimal scheduling of air conditioning load and electric vehicle under demand response

2021 ◽  
Vol 257 ◽  
pp. 01058
Author(s):  
Haiyu Huang ◽  
Chunming Wang ◽  
Shaolian Xia ◽  
Huaqiang Xiong ◽  
Baofeng Jiang ◽  
...  
Keyword(s):  
Electric Vehicles ◽  
Demand Response ◽  
Air Conditioning ◽  
Power Grid ◽  
Peak Load ◽  
Optimal Scheduling ◽  
Peak Shaving ◽  
Energy Internet ◽  
Direct Load Control ◽  
Scheduling Model

As an important part of energy Internet carrier, demand side resources can participate in many interactions with power grid. In order to reduce the peak to valley load difference of power grid, from the perspective of tapping the combined peak shaving potential of air conditioning load and electric vehicles, guided by TOU price and direct load control, this paper proposes an optimal scheduling model with the minimum load difference and the maximum total revenue of users as the objective function. The results show that the joint optimal scheduling strategy can reduce the peak load and eliminate the “secondary peak load” caused by disorderly charging of electric vehicles.

scholarly journals Developing an Algorithm to Consider Mutliple Demand Response Objectives

2018 ◽  
Vol 8 (1) ◽  
pp. 2621-2626 ◽  
Author(s):  
D. Behrens ◽  
T. Schoormann ◽  
R. Knackstedt
Keyword(s):  
Electric Vehicles ◽  
Demand Response ◽  
Peak Load ◽  
Cost Savings ◽  
Research Field ◽  
Multiple Objectives ◽  
Load Reduction ◽  
Solution Strategy ◽  
Research And Practice ◽  
Technological Improvement

Due to technological improvement and changing environment, energy grids face various challenges, which, for example, deal with integrating new appliances such as electric vehicles and photovoltaic. Managing such grids has become increasingly important for research and practice, since, for example, grid reliability and cost benefits are endangered. Demand response (DR) is one possibility to contribute to this crucial task by shifting and managing energy loads in particular. Realizing DR thereby can address multiple objectives (such as cost savings, peak load reduction and flattening the load profile) to obtain various goals. However, current research lacks algorithms that address multiple DR objectives sufficiently. This paper aims to design a multi-objective DR optimization algorithm and to purpose a solution strategy. We therefore first investigate the research field and existing solutions, and then design an algorithm suitable for taking multiple objectives into account. The algorithm has a predictable runtime and guarantees termination.

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