Browsing by Author "Ali, Abdelfatah"

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Enhancing hosting capacity of intermittent wind turbine systems using bi-level optimisation considering OLTC and electric vehicle charging stations
(2021-02-22) Ali, Abdelfatah; Mahmoud, Karar; Lehtonen, Matti
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Worldwide, the hosting capacity of renewable energy sources (RES) is remarkably expanded in distribution systems. One of the most auspicious RES is wind turbine systems (WTSs), which can improve the performance of distribution systems. In turn, the integration of high WTS penetrations can also deviate the system operation away from the standard condition. To tackle this issue, we propose a method for enhancing the hosting capacity of multiple WTSs considering their intermittent generations in distribution systems. The proposed method considers the operation of the on-load tap changer (OLTC), allowing to solve voltage problems efficiently. Especially, the proposed method optimises the charging/discharging power of electric vehicles (EVs), which can contribute positively to regulating WTS intermittent generation. Additionally, the reactive power support of WTSs, complying with the IEEE 1547:2018 standard, is incorporated in the planning model of WTSs. To solve such an optimisation problem, a bi-level optimisation algorithm is developed based on the gravitational search algorithm. Comprehensive simulation results are performed on the 69-bus distribution feeder interconnected to four EV stations. Based on the results, the proposed approach can efficiently enhance/increase the hosting capacity of WTSs in distribution systems, thanks to the consideration of OLTC, reactive power support of WTSs and EVs.
Maximizing Hosting Capacity of Uncertain Photovoltaics by Coordinated Management of OLTC, VAr Sources and Stochastic EVs
(2021-05) Ali, Abdelfatah; Mahmoud, Karar; Lehtonen, Matti
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
The interest in maximizing the hosting capacity of photovoltaics is recently being enlarged globally. This paper proposes a novel stochastic approach for maximizing the hosting capacity of photovoltaics in distribution systems. The proposed approach is based on a coordinated management scheme of control devices in distribution systems, i.e. transformer taps and VAr sources. It also considers the promising electrical vehicles with their stochastic nature and comprehensive model, including the arriving and departing times, and initial and preset conditions of their batteries state of charge. Further, the planning model of photovoltaics considers the reactive power support of the photovoltaic inverter based on the recently released IEEE 1547:2018 standard. Compared to existing approaches, the unique merit of the proposed approach is its ability to maximize the hosting capacity of photovoltaics by simultaneous optimization of the different control variables. To accurately solve this stochastic optimization model, a double-layer metaheuristic optimizer is developed for maximizing the hosting capacity of photovoltaics and addressing all constraints. The inner level of the optimizer optimizes the charging/discharging power of electric vehicles, transformer taps, and reactive power support while the outer one maximizes the sizes of photovoltaics. To assess the effectiveness of the proposed approach, various scenarios are performed on the IEEE 69-bus distribution system. The proposed approach can maximize the hosting capacity of photovoltaics while optimally managing transformers, VAr sources, and electric vehicles in a coordinated manner.
Multi-objective optimal planning of EV charging stations and renewable energy resources for smart microgrids
(2023-03) Asaad, Ali; Ali, Abdelfatah; Mahmoud, Karar; Shaaban, Mostafa F.; Lehtonen, Matti; Kassem, Ahmed M.; Ebeed, Mohamed
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Distribution system planners and operators have increasingly exposed great attention to maximizing the penetration of renewable energy resources (RERs), and electric vehicles (EVs) toward modern microgrids. Accordingly, intensive operational and economic problems are expected in such microgrids. Specifically, the operators need to meet the increased demand for EVs and increase the dependence on RERs. The charging strategy for EVs and the RER penetration level may result in increased power loss, thermal loading, voltage deviation, and overall system cost. To address these concerns, this paper proposed an optimal planning approach for allocating EV charging stations with controllable charging and hybrid RERs within multi-microgrids, where the charging strategy in the proposed planning approach contributed to improving power quality and overall system cost, where the voltage deviation, energy not supplied, total cost have been reduced to 26.03%, 49.57%, and 70.45%, respectively. The simulation results are compared with different optimization techniques to verify the effectiveness of the proposed algorithm. The proposed simultaneous allocation approach of EV charging stations and RERs can reduce operating costs for RERs and conventional stations while increasing the charging stations' capacity.
Multi-objective Photovoltaic Sizing with Diverse Inverter Control Schemes in Distribution Systems Hosting EVs
(2021-09) Ali, Abdelfatah; Mahmoud, Karar; Lehtonen, Matti
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Worldwide, photovoltaic (PV) and electric vehicles (EVs) have intensively been integrated into distribution systems. As a result, different operational issues can be observed due to PV generation variability and EV stochastic characteristics. In this article, an optimal sizing approach of multiple PVs in the existence of EVs is proposed. The proposed approach minimizes both the total voltage deviations and overall energy losses, prevents active PV power curtailment, and considers numerous constraints of PV, EV, and the distribution system. The features of the proposed approach are the considerations of PV, EV, and load uncertainties via incorporating their probabilistic models. Besides, it models arrival/parting times of EVs, the required state of charge (SOC) of EV batteries based on initial SOCs and remaining parking periods, and controlled/uncontrolled charging. Furthermore, diverse control schemes of the interfacing PV inverter are formulated in the proposed optimization model. To effectively solve this comprehensive model with conflicting subfunctions and variables, a two-level multiobjective evolutionary algorithm based on decomposition with fuzzy sets is developed. The upper optimization level accurately optimizes the sizes of multiple PVs, while the lower one optimizes charging/discharging of EV batteries, PV inverter oversize, and PV reactive power. The results prove the effectiveness of the proposed approach.
Optimal Allocation of Inverter-Based WTGS Complying with their DSTATCOM Functionality and PEV Requirements
(2020) Ali, Abdelfatah; Mahmoud, Karar; Raisz, David; Lehtonen, Matti
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Recently, the integration of inverter-based wind turbine generation systems (WTGS) and plug-in electric vehicles (PEV) has remarkably been expanded into distribution systems throughout the world. These distributed resources could have various technical benefits to the grid. However, they are also associated with potential operation problems due to their stochastic nature, such as high power losses and voltage deviations. An optimization-based approach is introduced in this paper to properly allocate multiple WTGS in distribution systems in the presence of PEVs. The proposed approach considers 1) uncertainty models of WTGS, PEV, and loads, 2) DSTATCOM functionality of WTGS, and 3) various system constraints. Besides, the realistic operational requirements of PEVs are addressed, including initial and preset conditions of their state of charge (SOC), arriving and departing times, and various controlled/uncontrolled charging schemes. The WTGS planning paradigm is established as a bi-level optimization problem which guarantees the optimal integration of multiple WTGS, besides optimized PEV charging in a simultaneous manner. For this purpose, a bi-level metaheuristic algorithm is developed for solving the planning model. Intensive simulations and comparisons with various approaches on the 69-bus distribution system interconnected with four PEV charging stations are deeply presented considering annual datasets. The results reveal the effectiveness of the proposed approach.
Optimal Placement and Sizing of Uncertain PVs Considering Stochastic Nature of PEVs
(2020-07) Ali, Abdelfatah; Raisz, David; Mahmoud, Karar; Lehtonen, Matti
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Recently, the penetration of photovoltaic (PV) units and plug-in electric vehicles (PEVs) has been quickly increased worldwide. Due to the intermittent nature of PV and the stochastic nature of PEVs, several operation problems can be noticed in distribution systems, including excessive energy losses and voltage violations. In this paper, an optimization-based algorithm is proposed to accurately determine the optimal locations and capacities of multiple PV units in the presence of PEVs to minimize energy losses while considering various system constraints. The proposed algorithm considers the uncertainty of PV and loads, and the stochastic nature of PEVs. Furthermore, the operational constraints of PEVs are incorporated in the optimization model: 1) arrival and departure times, 2) initial state of charge (SOC), 3) minimum preset state of charge by the owner, and 4) the time-of-use electricity tariff, and 5) different charging control schemes. The optimal PV planning model is formulated as a two-layer optimization problem that ensures an optimal PV allocation while optimizing PEV charging simultaneously. A two-layer metaheuristic method is developed to solve the optimization model considering annual datasets of the studied distribution systems. The results demonstrate the efficacy of the proposed algorithm.
Optimal planning of inverter-based renewable energy sources towards autonomous microgrids accommodating electric vehicle charging stations
(2022-01) Ali, Abdelfatah; Mahmoud, Karar; Lehtonen, Matti
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Renewable energy sources have recently been integrated into microgrids that are in turn connected to electric vehicle (EV) charging stations. In this regard, the optimal planning of microgrids is challenging with such uncertain generation and stochastic charging/discharging EV models. To achieve such ambitious goals, the best sites and sizes of photovoltaic and wind energy units in microgrids with EV are accurately determined in this work using an optimization technique. This proposed technique considers 1) generation profile uncertainty in photovoltaic and wind energy units as well as the total load demand, 2) photovoltaic and wind generation units' DSTATCOM operation capability, and 3) various branch and node constraints in the microgrid. Most importantly, the possible EV requirements are also taken into account, including initial and predetermined state of charge (SOC) arrangements, arrival and departure hours, and diverse regulated and unregulated charging strategies. A bi-level metaheuristic-based solution is established to address this complex planning model. The outer level and inner-level functions optimize renewable energy sources and EV decision variables. Sub-objectives to be optimized voltage deviations as well as grid power. The results demonstrate the effectiveness of the introduced method for planning renewable energy sources and managing EV to effectively achieve autonomous microgrids.
Optimal Sizing and Placement of Multiple Photovoltaics Considering Electric Vehicles Charging Stations
(2021-11-18) Ali, Abdelfatah; Mahmoud, Karar; Shaaban, Mostafa F.; Lehtonen, Matti
A4 Artikkeli konferenssijulkaisussa
Due to the fluctuated generation of Photovoltaic (PV) and stochastic charging and discharging schemes of electric vehicles (EVs), risky operational issues are observed in the medium-voltage distribution systems. Expected issues are excessive energy losses, voltage drop and voltage rise, and disruptions of operational boundaries in power distribution systems. To tackle these issues, we propose an optimization planning model in this paper for optimally allocate PV to accommodate EV charging stations in distribution systems. The proposed planning approach has the ability to determine the best PV locations and sizes to reduce the electrical energy losses in the distribution system, considering voltage and power flow boundaries. Consequently, the allocation problem of PV is created as a double-layer optimization model, which is solved by the gravitational search algorithm. To indicate the effectiveness of the proposed allocation approach of PV, different simulations are performed in the IEEE 69-bus distribution system. The computed results demonstrate that the proposed planning approach can optimally allocate multiple PV units while satisfying the EV charging requirements.
Optimization of Photovoltaic and Wind Generation Systems for Autonomous Microgrids with PEV-parking lots
(2021-08-13) Ali, Abdelfatah; Mahmoud, Karar; Lehtonen, Matti
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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.
Probabilistic Approach for Hosting High PV Penetration in Distribution Systems via Optimal Oversized Inverter With Watt-Var Functions
(2021-03) Ali, Abdelfatah; Mahmoud, Karar; Raisz, David; Lehtonen, Matti
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Accommodating a high penetration of intermittent photovoltaic (PV) in distribution systems can potentially cause several operational problems, most importantly, voltage violations. An optimal probabilistic approach is proposed in this article to optimally host high penetrations of PV units considering their stochastic nature. A benefit of the proposed approach is that it provides wider planning options since it optimizes the interfacing inverter oversize with smart watt-var functionalities. These smart functionalities include: 1) active power curtailment and 2) inverter reactive power. The utilization of these functionalities in the optimization model yields an optimal PV hosting that maximizes the benefits to distribution systems. The optimal probabilistic model of PV incorporates the probabilities of the PV power output and load while optimizing the inverter oversize and the two functionalities simultaneously. The proposed approach complies with the recently released IEEE 1547:2018 standard which regulates the reactive power support via the interfacing PV inverters. The efficacy of the proposed approach is demonstrated by comparisons with existing approaches. The results confirm the superiority of the proposed approach to optimally accommodate high PV penetration at single or multiple locations while minimizing voltage violations. The proposed approach is also applied to maximize the hosting capacity of PV.
Voltage fluctuation smoothing in distribution systems with RES considering degradation and charging plan of EV batteries
(2019-11-01) Ali, Abdelfatah; Raisz, David; Mahmoud, Karar
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Recently, the use of renewable energy sources (RES) and electric vehicles (EVs) has been rapidly increased worldwide. As a result of the highly fluctuating nature of RES, the charging and discharging rates of EVs significantly have to be increased, and so the lifespan of EV batteries decreases. In this paper, an optimization-based method is proposed to smooth voltage fluctuations due to various RES types by optimally controlling the charging and discharging power of EVs and the reactive power of the RES inverters. To extend the lifespan of the EV battery, EV power fluctuations and their minimum preset state of charge (SOC) are considered in the proposed optimization model. For this purpose, a new multi-objective function is formulated, including (1) voltage fluctuations, (2) EV power fluctuations, and (3) the deviation of SOC of EVs from their minimum desired level. The use of the hull moving average (HMA) is proposed to mitigate voltage fluctuations, which eliminates the lag problem of the widely used moving average methods. The gravitational search algorithm (GSA) is utilized to accurately solve the optimization model. The simulation results demonstrate the effectiveness of the proposed method to smooth voltage fluctuations while considering degradation and charging plan of EV batteries.