Optimal distributed generation and battery energy storage units integration in distribution systems considering power generation uncertainty

Khasanov, Mansur; Kamel, Salah; Rahmann, Claudia; Hasanien, Hany M.; Al‐Durra, Ahmed

doi:10.1049/gtd2.12230

Cited by 52 publications

(51 citation statements)

References 63 publications

(162 reference statements)

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“…The authors in [29] proposed rider optimization algorithm to optimum allocation of PV (Type-I), WT (Type-III), biomass and battery energy storage systems on IEEE 33 and 69-bus RDNs for daily profiles of load and generation.…”

Section: B Literature Reviewmentioning

confidence: 99%

Optimal Allocation of Renewable Distributed Generations Using Heuristic Methods to Minimize Annual Energy Losses and Voltage Deviation Index

Pürlü

Türkay

2022

IEEE Access

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In this paper, two metaheuristic methods, genetic algorithm and particle swarm optimization, are proposed to determine the optimal locations, sizes and power factors of single and double distributed generation units. In line with the 2050 carbon neutral goal, the aim was to integrate renewable distributed energy sources such as photovoltaic panels and wind turbines into the distribution system with a high penetration level. In contrast to most studies based on constant loads and dispatchable generations, an application considering the seasonal uncertainties of generation and consumption was performed to minimize the annual energy losses and voltage deviations of the distribution network. In addition, dispatchable, controllable and fuel-based conventional resources were allocated to compare the contributions of renewable resources. These seasonal case studies with various constraints were applied to IEEE 33-bus radial distribution network. To verify the feasibility and robustness of the proposed algorithms, case studies for peak loads were created and compared with the literature studies. While all distributed generation sources were operated at both unity and optimum power factor in all case studies, zero power factor and leading power factor scenarios were examined for a peak load only. Photovoltaic applications without energy storage technologies have not been very efficient because of the uneven daily distribution of solar irradiance, especially insufficient irradiation in the evening and excessive irradiation at noon. However, wind energy applications are more reliable and feasible, because the wind speed distribution is relatively more uniform than that of solar irradiation, both seasonally and daily. In all cases, operating distributed generation sources at the optimal power factor provided better results than those operating at unity power factor. As a result, wind turbines operating at optimal power factors have been found to contribute better than photovoltaic systems and are almost as good as conventional sources with controllable power output. While both proposed algorithms yielded better results than those in the literature, particle swarm optimization was better than genetic algorithm in terms of providing the best solution, faster convergence and shorter running time.

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Section: B Literature Reviewmentioning

confidence: 99%

Optimal Allocation of Renewable Distributed Generations Using Heuristic Methods to Minimize Annual Energy Losses and Voltage Deviation Index

Pürlü

Türkay

2022

IEEE Access

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“…Amongst, the scenariobased stochastic method is one of the most applied methods in various researches [32][33][34][35][36]. Generally, wind speed uncertainty can be modelled using normal [37] or Weibull [2] probability distribution functions. Moreover, a multi-band uncertainty set of wind power has been defined to combine the probability distribution characteristics of wind and load prediction errors [6,10].…”

Section: Multiple Uncertainties Scenariosmentioning

confidence: 99%

“…In recent years, the integration of renewable energy sources (RESs) has significantly as well as rapidly increased in the power systems generation sectors [1,2]. The proliferation of RESs with their intermittent generation outputs has demanded a higher degree of operational system flexibility [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Harnessing power system flexibility under multiple uncertainties

Mazaheri

Saber

Fattaheian‐Dehkordi

et al. 2022

IET Generation Trans & Dist

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Increasing the intermittent outputs of renewable energy sources (RESs) has forced planners to define a new concept named flexibility. In this regard, some short‐ and long‐term solutions, such as transmission expansion planning (TEP) and energy storage systems (ESSs) have been suggested to improve the flexibility amount. A proper optimization procedure is required to choose an optimal solution to improve flexibility. Therefore, a mixed‐integer linear programming (MILP) direct‐optimization TEP versus ESSs co‐planning model is presented in this paper to enhance power system flexibility. In doing so, a novel RES‐BESS‐based grid‐scale system flexibility metric is proposed to investigate the improvement of flexibility amount via ESSs modules in the numerical structure. In this paper, a novel repetitive fast offline method has been proposed to quickly reach the desired amount of flexibility by defining an engineering price/benefit trade‐off to finally find the best investment plan. Also, multiple uncertainties associated with wind farms and demanded loads and a practical module‐type battery energy storage system (BESS) structure for each node are defined. The proposed model is applied to the modified IEEE 73‐bus test system including wind farms, where the numerical results prove the model efficiency as BESS impacts on flexibility, investment plans and power system economics.

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“…To address these challenges, by combining norm-1 and norm-inf, a data-driven robust optimization model is proposed in Zhang et al, 2021b, which effectively constrains the spatiotemporal correlation of wind power. In Khasanov et al, 2021, a meta-heuristic rider optimization algorithm (ROA) is proposed to achieve optimal allocation of distributed power sources, coping with the challenges brought about by the uncertainties of distributed power sources and effectively improving the computational efficiency. At the same time, the optimal size and installation location of DGs are obtained based on the ROA calculation, which effectively improves the computational efficiency.…”

Section: Introductionmentioning

confidence: 99%

Robust optimization of the active distribution network involving risk assessment

Yang

Zhang

et al. 2022

Front. Energy Res.

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Inherent dynamic constraints of distributed generations (DGs) and the correlation between injected variables bring great challenges to distribution network operation. In order to improve the degree of coupling and interconnection coordination between different energy devices, improve the ability of the distribution network to cope with the uncertainty of DGs, achieve low-carbon operation, and improve the environmental friendliness of distribution network operation, this article proposes a robust optimization approach involving risk assessment. The semi-invariant method and scene clustering are used to deal with the uncertainty of DGs and load, thus formulating a robust optimization model for distribution network distribution based on risk indices. To address the time-varying constraints of energy storage systems (ESSs) and gas turbines, a two-stage box-based decomposition model is established. Dynamic constraints are included in the first stage to constrain the operating state and operating domain of the unit and ESSs. In the second stage, the multi-timescale optimization problem is transformed into multiple single-timescale optimization problems, which are solved by the column and constraint generation (C&CG) algorithm to improve the solution efficiency. The feasibility of the comprehensive optimization model based on dynamic reconfiguration and distributed robust optimization (DRO) is demonstrated with the PG&E 69 bus system.

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Optimal distributed generation and battery energy storage units integration in distribution systems considering power generation uncertainty

Cited by 52 publications

References 63 publications

Optimal Allocation of Renewable Distributed Generations Using Heuristic Methods to Minimize Annual Energy Losses and Voltage Deviation Index

Optimal Allocation of Renewable Distributed Generations Using Heuristic Methods to Minimize Annual Energy Losses and Voltage Deviation Index

Harnessing power system flexibility under multiple uncertainties

Robust optimization of the active distribution network involving risk assessment

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