Optimal GENCO's bidding strategy in a power exchange facilitating combined power and emission trading using Intelligent Programmed Genetic Algorithm

Shah, Devnath; Chatterjee, Saibal

doi:10.1002/2050-7038.12463

Cited by 10 publications

(9 citation statements)

References 56 publications

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“…[26], [27], [28] Artificial Neural Networks [11], [29], [30], [31], [32], [33], [34] [35], [12], [36], [37], [38], [39], [40] Reinforcement Learning [41], [42], [43], [44], [45] [46], [47], [48], [49], [50], [51], [52], [10], [53] Nature-Inspired Intelligence [54] [55]…”

Section: Overview Of Algorithmic Approaches For Electricity and Flexibility Tradingmentioning

confidence: 99%

“…The identified publications for electricity trading illustrate the range of existing nature-inspired algorithms. [46], [52], [10], [51], and [49] provide a NII approach to derive the best bidding strategy as an electricity supplier on a day-ahead market. [52] and [10] provide a Genetic Algorithm (GA).…”

Section: Overview Of Algorithmic Approaches For Electricity and Flexibility Tradingmentioning

confidence: 99%

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How to trade electricity flexibility using artificial intelligence - An integrated algorithmic framework

Hanny¹,

Körner²,

Leinauer³

et al. 2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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In course of the energy transition, the growing share of Renewable Energy Sources (RES) makes electricity generation more decentralized and intermittent. This increases the relevance of exploiting flexibility potentials that help balancing intermittent RES supply and demand and, thus, contribute to overall system resilience. Digital technologies, in the form of automated trading algorithms, may considerably contribute to flexibility exploitation, as they enable faster and more accurate market interactions. In this paper, we develop an integrated algorithmic framework that finds an optimal trading strategy for flexibility on multiple markets. Hence, our work supports the trading of flexibility in a multi-market environment that results in enhanced market integration and harmonization of economically traded and physically delivered electricity, which finally promotes resilience in highly complex electricity systems.

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Section: Overview Of Algorithmic Approaches For Electricity and Flexibility Tradingmentioning

confidence: 99%

Section: Overview Of Algorithmic Approaches For Electricity and Flexibility Tradingmentioning

confidence: 99%

How to trade electricity flexibility using artificial intelligence - An integrated algorithmic framework

Hanny¹,

Körner²,

Leinauer³

et al. 2022

Proceedings of the Annual Hawaii International Conference on System Sciences

View full text Add to dashboard Cite

show abstract

“…Standalone GA has been implemented in solving various engineering design problems ranging from simple to complex tasks and has proven its worth in the field of optimisation. There have been works on variants of GA as well for solving optimisation problems 38 . The algorithm has shown worthy merits in studies that involve multiple optimum solutions.…”

Section: Fem‐based Analysis Of Vibration and Acoustic Noisementioning

confidence: 99%

Investigation on magnetostrictive behaviour of a converter transformer influenced by dominant harmonics: AFEMandANNbased approach

Yadav

Mehta

2021

Int Trans Electr Energ Syst

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Summary The introduction of harmonics to the HVDC system is imminent, which affects the components of the power system from varied perspectives. The present work emphasises analysing the behaviour of the converter transformer concerning the harmonics in the input. The process involves designing a stepped configuration of a 240‐MVA converter transformer using the finite element method (FEM). The designed model is analysed as per its structural dynamics to obtain its vibrational response for different ranges over the frequency spectrum. Furthermore, the module of acoustics and thermal analysis is coupled to the vibrational model. The coupled model serves as an efficient tool in analysing the impact of different orders of harmonics on the vibrational, acoustic and thermal performance of the core of the converter transformer. The computational effort taken in solving the coupled field problem is simplified by designing a neural network model and the prediction efficacy of the network is enhanced by using a Hybrid of Particle Swarm Optimisation and Gravitational Search Algorithm (HPSOGWO) as tuner of the internal parameters of the neural network. The significance of the designed model is also examined against other well‐versed hybrid algorithms in the literature in terms of various statistical indices.

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“…In this paper, GA is utilized for parameter optimization. The GA has been successfully applied to a variety of problems including, wind power forecast error, 14 hybrid solar wind battery system, 15 optimal power exchange facilitating in GENCO's bidding strategy, 16 optimal design for wind‐resistant tall buildings, 17 respectively. In Reference 18, the auxiliary controller is designed for the wind power system to enhance system damping under various wind speeds and its parameter are tuned by using GA.…”

Section: Introductionmentioning

confidence: 99%

“…These optimization techniques have proven efficient for many applications in the optimal tuning of control gain parameters. The GA and PSO are considered the most established algorithm for optimization and taken into consideration as the benchmark for optimization techniques 6,8,11,14‐17 . In this work, GA is used to design and optimize the damping controller parameters because of their capability, versatility, faster convergence, and efficient complex problems.…”

Section: Introductionmentioning

confidence: 99%

Parameter tuning of PSS and STATCOM controllers using genetic algorithm for improvement of small‐signal and transient stability of power systems with wind power

Bhukya

Mahajan

2021

Int Trans Electr Energ Syst

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Summary This work demonstrates the impact and robust coordination control among the Doubly Induction Generator (DFIG) and Synchronous Generators (SGs) with Power Oscillation Damping (POD), Power System Stabilizer (PSS),and STATic synchronous COMpensator (STATCOM) on the critical Low‐Frequency Oscillations (LFOs). These modes occurred due to system uncertainty, which leads to power flow interruption and experiences instability. It is mitigated by the proposed location and optimal coordinated optimized gain parameters of the controller. The locations are determined using the deterministic method and power flow sensitivity analysis (PSS and STATCOM). The objective function for the Genetic Algorithm (GA) is chosen as the real part and damping ratio of eigenvalue deviation of rotor speed. The small‐signal stability is assessed by using eigenvalue analysis via linearization of a nonlinear system. The transient stability is performed through time‐domain simulation by creating a three‐phase fault. Both analyses are examined with and without POD, PSS, and STATCOM and their combination by substituting SG with the same capacity of Wind Farm (WF). It is also indicated that a combination of POD, PSS, and STATCOM enhances system stability. It is also observed that the WF could not significantly involve in the modes of oscillation alike to the SG but provides the damping and beneficial toward the stability. GA and optimal location improve the system damping characteristics over a wide range of system uncertainty. The effectiveness and robustness of the proposed approaches are verified by considering a two‐area modified test system. Highlights This work demonstrates the impact and robust coordination control among the DFIG and SGs with POD, PSS and STATCOM on the critical LFOs caused by the intermittent nature of wind power. The locations are determined using the deterministic method and power flow sensitivity analysis (PSS and STATCOM). The objective function for the GA is chosen as the real part and damping ratio of eigenvalue and rotor speed deviation. The obtained results show enhancement in system stability.

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Optimal GENCO's bidding strategy in a power exchange facilitating combined power and emission trading using Intelligent Programmed Genetic Algorithm

Cited by 10 publications

References 56 publications

How to trade electricity flexibility using artificial intelligence - An integrated algorithmic framework

How to trade electricity flexibility using artificial intelligence - An integrated algorithmic framework

Investigation on magnetostrictive behaviour of a converter transformer influenced by dominant harmonics: AFEMandANNbased approach

Parameter tuning of PSS and STATCOM controllers using genetic algorithm for improvement of small‐signal and transient stability of power systems with wind power

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