The effect of rescheduling power plants and optimal allocation of STATCOM in order to Improve power system static security using TLBO algorithm

The grid integration of renewable energy sources interfaced through power electronic converters is undergoing a significant acceleration to meet environmental and political targets. The rapid deployment of converters brings new challenges in ensuring robustness, transient stability, among others. In order to enhance transient stability, transmission system operators established network grid code requirements for converter-based generators to support the primary control task during faults. A critical factor in terms of implementing grid codes is the control strategy of the grid-side converters. Grid-forming converters are a promising solution which could perform properly in a weak-grid condition as well as in an islanded operation. In order to ensure grid code compliance, a wide range of transient stability studies is required. Time-domain simulations are common practice for that purpose. However, performing traditional monolithic time domain simulations (single solver, single domain) on a converter-dominated power system is a very complex and computationally intensive task. In this paper, a co-simulation approach using the mosaik framework is applied on a power system with grid-forming converters. A validation workflow is proposed to verify the co-simulation framework. The results of comprehensive simulation studies show a proof of concept for the applicability of this co-simulation approach to evaluate the transient stability of a dominant grid-forming converter-based power system.

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Impact on Power System Loadability with High Participation of Wind Power Plants

2020 IEEE/PES Transmission and Distribution Conference and Exposition (T&D) ◽

10.1109/td39804.2020.9299948 ◽

2020 ◽

Author(s):

Juan C. Mecon ◽

Mario A. Rios

Keyword(s):

Power System ◽

Wind Power ◽

Power Plants ◽

Wind Power Plants ◽

High Participation

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Wind integrated power system to reduce emission: An application of Bat algorithm

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189770 ◽

2021 ◽

pp. 1-9 ◽

Cited By ~ 1

Author(s):

B. Venkateswara Rao ◽

Ramesh Devarapalli ◽

H. Malik ◽

Sravana Kumar Bali ◽

Fausto Pedro García Márquez ◽

...

Keyword(s):

Power Systems ◽

Power System ◽

Power Plants ◽

Optimal Parameter ◽

Electrical Power ◽

Bat Algorithm ◽

Voltage Profile ◽

Electrical Power Systems ◽

Generation Cost ◽

Automation Technology

The trend of increasing demand creates a gap between generation and load in the field of electrical power systems. This is one of the significant problems for the science, where it require to add new generating units or use of novel automation technology for the better utilization of the existing generating units. The automation technology highly recommends the use of speedy and effective algorithms in optimal parameter adjustment for the system components. So newly developed nature inspired Bat Algorithm (BA) applied to discover the control parameters. In this scenario, this paper considers the minimization of real power generation cost with emission as an objective. Further, to improve the power system performance and reduction in the emission, two of the thermal plants were replaced with wind power plants. In addition, to boost the voltage profile, Static VAR Compensator (SVC) has been integrated. The proposed case study, i.e., considering wind plant and SVC with BA, is applied on the IEEE30 bus system. Due to the incorporation of wind plants into the system, the emission output is reduced, and with the application of SVC voltage profile improved.

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Optimal Allocation of Multiple Types of Distributed Generations in Radial Distribution Systems Using a Hybrid Technique

Sustainability ◽

10.3390/su13126644 ◽

2021 ◽

Vol 13 (12) ◽

pp. 6644

Author(s):

Ali Selim ◽

Salah Kamel ◽

Amal A. Mohamed ◽

Ehab E. Elattar

Keyword(s):

Power System ◽

Radial Distribution ◽

Distribution Systems ◽

Optimal Allocation ◽

Optimization Technique ◽

Mathematical Formulation ◽

Optimization Techniques ◽

Sensitivity Factor ◽

Hybrid Technique ◽

Analytical Technique

In recent years, the integration of distributed generators (DGs) in radial distribution systems (RDS) has received considerable attention in power system research. The major purpose of DG integration is to decrease the power losses and improve the voltage profiles that directly lead to improving the overall efficiency of the power system. Therefore, this paper proposes a hybrid optimization technique based on analytical and metaheuristic algorithms for optimal DG allocation in RDS. In the proposed technique, the loss sensitivity factor (LSF) is utilized to reduce the search space of the DG locations, while the analytical technique is used to calculate initial DG sizes based on a mathematical formulation. Then, a metaheuristic sine cosine algorithm (SCA) is applied to identify the optimal DG allocation based on the LSF and analytical techniques instead of using random initialization. To prove the superiority and high performance of the proposed hybrid technique, two standard RDSs, IEEE 33-bus and 69-bus, are considered. Additionally, a comparison between the proposed techniques, standard SCA, and other existing optimization techniques is carried out. The main findings confirmed the enhancement in the convergence of the proposed technique compared with the standard SCA and the ability to allocate multiple DGs in RDS.

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Optimal Scheduling of a Regional Power System Aiming at Accommodating Clean Energy

Sustainability ◽

10.3390/su13042169 ◽

2021 ◽

Vol 13 (4) ◽

pp. 2169

Author(s):

Xing Chen ◽

Suhua Lou ◽

Yanjie Liang ◽

Yaowu Wu ◽

Xianglu He

Keyword(s):

Power System ◽

Optimal Allocation ◽

Clean Energy ◽

Optimal Scheduling ◽

Regional Power ◽

Penalty Cost ◽

Purchase Cost ◽

Efficient Resource ◽

Distribution Of Resources ◽

Tie Line

The regional power system is an essential mechanism to solve the unbalanced distribution of resources and achieve more efficient resource allocation. In this paper, an optimal scheduling model of the regional power system is developed, to maximize social welfare and minimize clean energy electricity curtailment. This model can realize the optimal allocation of power generation resources and the maximum accommodation of multiple types of clean energy, by minimizing the sum of the electricity purchase cost and the dynamic penalty cost of clean energy. Meanwhile, it considers the modeling of the key AC/DC hybrid tie-line in the regional power grid. To this end, the modeling methods of power transmitted by AC/DC tie-line, the net loss of the tie-line, the stair-like operation of the DC tie-line power, the operation constraints of the DC tie-line are proposed. Then a simulation example study is conducted to verify the effectiveness of the model, which proves that the regional power system can stimulate the resource optimization potential better than the provincial power system.

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Co-Simulation Framework for Optimal Allocation and Power Management of DGs in Power Distribution Networks Based on Computational Intelligence Techniques

Electronics ◽

10.3390/electronics10141648 ◽

2021 ◽

Vol 10 (14) ◽

pp. 1648

Author(s):

Marinko Barukčić ◽

Toni Varga ◽

Vedrana Jerković Jerković Štil ◽

Tin Benšić

Keyword(s):

Power System ◽

Power Management ◽

Computational Intelligence ◽

Input Data ◽

Optimization Problem ◽

Optimal Allocation ◽

Distribution Networks ◽

Distributed Generations ◽

Data Resolution ◽

The Impact

The paper researches the impact of the input data resolution on the solution of optimal allocation and power management of controllable and non-controllable renewable energy sources distributed generation in the distribution power system. Computational intelligence techniques and co-simulation approach are used, aiming at more realistic system modeling and solving the complex optimization problem. The optimization problem considers the optimal allocation of all distributed generations and the optimal power control of controllable distributed generations. The co-simulation setup employs a tool for power system analysis and a metaheuristic optimizer to solve the optimization problem. Three different resolutions of input data (generation and load profiles) are used: hourly, daily, and monthly averages over one year. An artificial neural network is used to estimate the optimal output of controllable distributed generations and thus significantly decrease the dimensionality of the optimization problem. The proposed procedure is applied on a 13 node test feeder proposed by the Institute of Electrical and Electronics Engineers. The obtained results show a huge impact of the input data resolution on the optimal allocation of distributed generations. Applying the proposed approach, the energy losses are decreased by over 50–70% by the optimal allocation and control of distributed generations depending on the tested network.

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