Multiobjective Photovoltaic Sizing With Diverse Inverter Control Schemes in Distribution Systems Hosting EVs

Ali, Abdelfatah; Mahmoud, Karar; Lehtonen, Matti

doi:10.1109/tii.2020.3039246

Cited by 30 publications

(11 citation statements)

References 28 publications

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“…During the parking time of the PEVs, the option of vehicle-to-grid (V2G) technology can be employed, while enough SOC for each PEV at its parting time for daily mileages should be guaranteed. Here, the maximum charging and discharging rate of each PEV is 50% of the battery capacity (0.5Cbatt, n) [8]. Two different types of DGs (PV and wind) are optimally allocated in the microgrid to minimize the annual energy losses and the tie-line power between the utility power grid and microgrid.…”

Section: A Test Systemmentioning

confidence: 99%

Optimization of Photovoltaic and Wind Generation Systems for Autonomous Microgrids With PEV-Parking Lots

Ali

Mahmoud

Lehtonen

2022

IEEE Systems Journal

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Lately, the integration of renewable energy sources (e.g. photovoltaic and wind generation systems) has been raised into microgrids interconnected with plug-in electric vehicles (PEV). Such intermittent generation and charging/discharging PEV profiles are challenging to ensure the secure and optimal operation of microgrids. In this paper, an optimization approach is proposed to determine the optimal locations and sizes of photovoltaic and wind generation systems in microgrids with PEV-parking lots. The developed approach addresses 1) the uncertainty of generation profiles of photovoltaic and wind generation systems and loads, 2) the DSTATCOM feature of photovoltaic and wind generation systems, and 3) microgrid constraints. The feasible PEV conditions are also considered, i.e. initial and preset conditions of their state of charge (SOC), arriving and departing times, and various controlled/uncontrolled charging schemes. To solve the planning model, we have developed a bi-level metaheuristic-based approach. The upper-level and lower-level optimization tasks are to optimize the decision variables of renewable energy sources and PEV, respectively. Both energy losses through the lines and energy from the main grid are considered as sub-objectives to be minimized. Various simulations and study cases are performed to assess the effectiveness of the proposed approach. The outcomes show the efficacy of the proposed approach to simultaneously plan renewable energy sources and manage PEV to form an autonomous microgrid.

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Section: A Test Systemmentioning

confidence: 99%

Optimization of Photovoltaic and Wind Generation Systems for Autonomous Microgrids With PEV-Parking Lots

Ali

Mahmoud

Lehtonen

2022

IEEE Systems Journal

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“…Classification Control Algorithm [7,10] Rule-based Operating mode control [8,9] Thermostat control [11] State machine control [10,[12][13][14] Fuzzy control [16] Optimization-based Particle swarm optimization [17,18] ECMS [19,20] DP [21] PMP…”

Section: Refmentioning

confidence: 99%

“…The rule-based strategies have strong practicability and high reliability [7][8][9][10][11][12][13][14][15]. However, most of them are based on the engineering experience, and the results are dependent on the design of the rules.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Two Energy Management Strategies Considering Power System Durability for PEMFC-LIB Hybrid Logistics Vehicle

Liang

Jia

et al. 2021

Energies

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For commercial applications, the durability and economy of the fuel cell hybrid system have become obstacles to be overcome, which are not only affected by the performance of core materials and components, but also closely related to the energy management strategy (EMS). This paper takes the 7.9 t fuel cell logistics vehicle as the research object, and designed the EMS from two levels of qualitative and quantitative analysis, which are the composite fuzzy control strategy optimized by genetic algorithm and Pontryagin’s minimum principle (PMP) optimized by objective function, respectively. The cost function was constructed and used as the optimization objective to prolong the life of the power system as much as possible on the premise of ensuring the fuel economy. The results indicate that the optimized PMP showed a comprehensive optimal performance, the hydrogen consumption was 3.481 kg/100 km, and the cost was 13.042 $/h. The major contribution lies in that this paper presents a method to evaluate the effect of different strategies on vehicle performance including fuel economy and durability of the fuel cell and battery. The comparison between the two totally different strategies helps to find a better and effective solution to reduce the lifetime cost.

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“…It was found that the main element for electrochemical performance improvement was the self-heating in the large-sized cells. In the recent years, advanced artificial intelligence and machine learning algorithms have been utilized in many worldwide practical applications with superior performance in diverse power system topics, e.g., [13][14][15][16][17][18][19][20] while renewables and storage systems are simulated intensively [21][22][23][24][25]. In [26], a single-particle model (SPM) was used to introduce a physical-chemical model of the lithium-ion battery, which governed its performance.…”

Section: Introductionmentioning

confidence: 99%

Reliable and Robust Observer for Simultaneously Estimating State-of-Charge and State-of-Health of LiFePO4 Batteries

et al. 2021

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Batteries are everywhere, in all forms of transportation, electronics, and constitute a method to store clean energy. Among the diverse types available, the lithium-iron-phosphate (LiFePO4) battery stands out for its common usage in many applications. For the battery’s safe operation, the state of charge (SOC) and state of health (SOH) estimations are essential. Therefore, a reliable and robust observer is proposed in this paper which could estimate the SOC and SOH of LiFePO4 batteries simultaneously with high accuracy rates. For this purpose, a battery model was developed by establishing an equivalent-circuit model with the ambient temperature and the current as inputs, while the measured output was adopted to be the voltage where current and terminal voltage sensors are utilized. Another vital contribution is formulating a comprehensive model that combines three parts: a thermal model, an electrical model, and an aging model. To ensure high accuracy rates of the proposed observer, we adopt the use of the dual extend Kalman filter (DEKF) for the SOC and SOH estimation of LiFePO4 batteries. To test the effectiveness of the proposed observer, various simulations and test cases were performed where the construction of the battery system and the simulation were done using MATLAB. The findings confirm that the best observer was a voltage-temperature (VT) observer, which could observe SOC accurately with great robustness, while an open-loop observer was used to observe the SOH. Furthermore, the robustness of the designed observer was proved by simulating ill-conditions that involve wrong initial estimates and wrong model parameters. The results demonstrate the reliability and robustness of the proposed observer for simultaneously estimating the SOC and SOH of LiFePO4 batteries.

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Multiobjective Photovoltaic Sizing With Diverse Inverter Control Schemes in Distribution Systems Hosting EVs

Cited by 30 publications

References 28 publications

Optimization of Photovoltaic and Wind Generation Systems for Autonomous Microgrids With PEV-Parking Lots

Optimization of Photovoltaic and Wind Generation Systems for Autonomous Microgrids With PEV-Parking Lots

Comparison of Two Energy Management Strategies Considering Power System Durability for PEMFC-LIB Hybrid Logistics Vehicle

Reliable and Robust Observer for Simultaneously Estimating State-of-Charge and State-of-Health of LiFePO4 Batteries

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