Enhancing hosting capacity of intermittent wind turbine systems using bi‐level optimisation considering OLTC and electric vehicle charging stations

Ali, Abdelfatah; Mahmoud, Karar; Lehtonen, Matti

doi:10.1049/iet-rpg.2020.0837

Cited by 25 publications

(20 citation statements)

References 35 publications

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“…The authors of [24] have introduced a multi-objective framework that aims to optimally plan PV and WT units considering their probabilistic models in distribution systems while optimizing costs as well as carbon emissions. The hosting capacity of PV and WT has been improved by utilizing coordinated control scheme and reactive power support in [25], [26]. Due to the current revolutions in creating and developing efficient metaheuristic optimization solvers, diverse types have been employed for RES planning in distribution systems, especially for multi-objective frameworks.…”

Section: Introductionmentioning

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: Introductionmentioning

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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“…Wide varieties have been used for RES planning in distribution systems, particularly for multi-objective structures, resulting from recent revolutions in implementing and sustaining effective meta-heuristic optimization problem solvers. Crow search algorithm autodrive particle swarm optimization method [17], gravitational search algorithm [18,19], tabu search optimization solver [20], genetic-based optimization method [21], artificial ecosystembased optimization method [22], equilibrium optimizer [23], simulated annealing optimization method [24], and ant colony optimization method [25] are some examples of optimization solvers. The authors of [26] have proposed an adaptive robust co-optimization method for capacity allocation and bidding approach of a prosumer interconnected with PV, WT, and a battery energy storage system.…”

Section: Introductionmentioning

confidence: 99%

Optimal planning of inverter‐based renewable energy sources towards autonomous microgrids accommodating electric vehicle charging stations

Ali

Mahmoud

Lehtonen

2021

IET Generation Trans & Dist

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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. INTRODUCTIONRenewable energy sources (RESs) are being more widely used around the world. Global policies to minimize greenhouse gas emissions drive this trend, but innovations in future electrical power generation technologies are anticipated [1][2][3]. Due to their elasticity and cost-effectiveness, photovoltaic (PV) and wind turbine (WT) are the most prominent RES variants. Remarkably, RESs have the potential to have a positive impact on distribution system efficiency. Such PV and WT systems, in particular, could improve supply efficiency, solve voltage problems, reduce power losses, improve power quality, and reduce the loading on traditional controlling units, thanks to their smart functions [4,5]. However, the PV and WT units' extremely intermittent generation could cause a slew of functional and operational issues, limiting their authorized accom-This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…In the DG hosting capacity problem, EVs have been scarcely studied; however, an initial approach is presented in [22], which estimates the maximum DG capacity to be installed considering EVs as uncontrollable loads. To overcome this limitation, some studies present different approaches to increase the DG installed capacity, taking advantage of controllable features of the EVs [23]- [25]. Although these works exploit, from the perspective of the DSO, the benefits that controllable features of EVs can provide in the EDS operation, these studies assume that EVs are placed in the system; therefore, the simultaneous hosting capacity assessment of DG and EVs in EDSs is not considered in such approaches.…”

Section: Introductionmentioning

confidence: 99%

“…Although there is a vast number of approaches that address the DG hosting capacity problem, only [23]- [25] consider the effects of EVs. As discussed in the literature review, approaches to simultaneously estimate the hosting capacity of DG and EVs in EDSs have been scarcely proposed, where only the work presented in [26] is found.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Distributed Generation and Electric Vehicles Hosting Capacity Assessment in Electric Distribution Systems

2021

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This work presents a strategy, from the perspective of the distribution system operator (DSO), that aims at simultaneously estimating the maximum penetration levels of renewable-based distributed generation (DG) and electric vehicles (EVs) that can be accommodated into an electric distribution system (EDS). To estimate such capacity, operational resources such as generation curtailment and controllable features of EVs can be managed to ensure the safe operation of the EDS and avoid infeasible operational conditions. Through a multi-period representation, the proposed strategy models the variability in demand consumption and DG power production. In addition, driving patterns of EV owners and energy requirements of EVs, obtained through probability density functions, are incorporated in this representation. Inherently, the problem is represented as an optimization model, and to determine its solution, an algorithm based on the metaheuristics greedy randomized adaptive search and tabu search (GRASP-TS) is developed. The applicability of the planning strategy is assessed on a 33-bus EDS under different test conditions and the numerical results show that higher penetrations of EVs and renewable-based DG can be accommodated without impacting the safe operation of the EDS. The results also demonstrate that by controlling the power draw by EV aggregators, an increase of 9% can be obtained in the DG installed capacity compared to the case of uncontrolled charging of EVs. In addition, the scalability of the proposed approach is studied using two distribution systems, the 83-bus system and the 135-bus system, where the results show that the convergence of the algorithm is achieved in a few iterations.INDEX TERMS Electric vehicle aggregator, GRASP-TS, hosting capacity, distributed generation.

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Enhancing hosting capacity of intermittent wind turbine systems using bi‐level optimisation considering OLTC and electric vehicle charging stations

Cited by 25 publications

References 35 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

Optimal planning of inverter‐based renewable energy sources towards autonomous microgrids accommodating electric vehicle charging stations

Simultaneous Distributed Generation and Electric Vehicles Hosting Capacity Assessment in Electric Distribution Systems

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