Low Carbon Multi-Objective Unit Commitment Integrating Renewable Generations

Yang, Dongsheng; Zhou, Xianyu; Yang, Zhile; Guo, Yuanjun; Niu, Qun

doi:10.1109/access.2020.3022245

Cited by 13 publications

(7 citation statements)

References 48 publications

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“…The VPE parameters of the cost functions are obtained from [58], and those of the emission functions are accessed from [21]. Moreover, the parameters specific to CO 2 and SO 2 for the tri-objective problems are drawn from [36]. Operational constraints, including generation capacity, minimum operating time durations, and a 10% reserve, remain the same across all test systems.…”

Section: Specifications Of Generating Unitsmentioning

confidence: 99%

“…The third category is to use hybrid approaches that combine mathematical and heuristic strategies. The whale optimization algorithm (WOA) [32], the particle swarm optimization (PSO) [33], the grey wolf optimization (GWO) [28,34], the non-dominated sorting genetic algorithm-II (NSGA-II) [35], the non-dominated sorting genetic algorithm-III (NSGA-III) [36], and the evolutionary algorithm based on decomposition (EAD) [37] are some well-known examples applied for power scheduling. Even though these methods integrate diverse strategies, they are still heuristic, and there is no guarantee that they can find global optima [31].…”

Section: Introductionmentioning

confidence: 99%

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Reinforcement Learning-Based Multi-Objective Optimization for Generation Scheduling in Power Systems

Ebrie,

Kim

2024

Systems

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Multi-objective power scheduling (MOPS) aims to address the simultaneous minimization of economic costs and different types of environmental emissions during electricity generation. Recognizing it as an NP-hard problem, this article proposes a novel multi-agent deep reinforcement learning (MADRL)-based optimization algorithm. Within a custom multi-agent simulation environment, representing power-generating units as collaborative types of reinforcement learning (RL) agents, the MOPS problem is decomposed into sequential Markov decision processes (MDPs). The MDPs are then utilized for training an MADRL model, which subsequently offers the optimal solution to the optimization problem. The practical viability of the proposed method is evaluated across several experimental test systems consisting of up to 100 units featuring bi-objective and tri-objective problems. The results demonstrate that the proposed MADRL algorithm has better performance compared to established methods, such as teaching learning-based optimization (TLBO), real coded grey wolf optimization (RCGWO), evolutionary algorithm based on decomposition (EAD), non-dominated sorting algorithm II (NSGA-II), and non-dominated sorting algorithm III (NSGA-III).

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Section: Specifications Of Generating Unitsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning-Based Multi-Objective Optimization for Generation Scheduling in Power Systems

Ebrie,

Kim

2024

Systems

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“…Multiobjective variants [1] of the unit commitment problem are crucial in the current day world. These problems aim to consider reduction of CO 2 emissions and penetration of renewable power in addition to cost optimization.…”

Section: Introductionmentioning

confidence: 99%

Benefits of Multiobjective Learning in Solar Energy Prediction

Kannan¹

2023

Preprint

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While the space of renewable energy forecasting has received significant attention in the last decade, literature has primarily focused on machine learning models that train on only one objective at a time. A host of classification (and regression) tasks in energy markets lead to highly imbalanced training data. Say, to balance reserves, it is natural for market regulators to have a choice to be more/less averse to false negatives (can lead to poor operating efficiency and costs) than to false positives (can lead to market shortfall). Besides accuracy, other metrics like algorithmic bias, RMBE (in regression problems), inferencing time, and model sparsity are also very crucial. This paper is amongst the firsts in the field of renewable energy forecasting that attempts to present a Pareto frontier of solutions (tradeoffs), that answers the question on handling multiple objectives by means of using the XGBoost model (Gradient Boosted Trees). Our proposed algorithm relies on using a sequence of weighted (uniform meshes) single objective model training routines. Real world data examples from the Amherst (Massachusetts, United States) solar energy prediction panels with both triobjective (focus on accuracy) and biojective (focus on fairness/bias) classification instances are considered. Numerical experiments appear promising and clear advantages over single objective methods are seen by observing the spread and variety of solutions (model configurations).

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“…Without considering uncertainty, the results do not have sufficient validity and accuracy [7]. In recent years, several studies have been done in the field of unit commitment of the power system to renewable sources [8][9][10]. Unit UC studies can be divided into six categories: traditional methods [11], innovative methods [12], artificial intelligence methods [13][14][15], stochastic methods [16], estimated control methods [17], and other theoretical methods [18].…”

Section: Introductionmentioning

confidence: 99%

Stochastic Unit Commitment Study in a Power System with Flexible Load in Presence of High Penetration Renewable Farms

Mojtahedzadeh Larijani,

Ahmadi Kamarposhti,

Nouri

2023

International Journal of Energy Research

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In this paper, a new hybrid multiobjective algorithm, namely, the modified bald eagle search Algorithm (MBES), integrated with the grasshopper optimization algorithm, is proposed to solve the unit commitment (UC) problem. We consider a standard 10-unit power system with two wind farms, two photovoltaic farms, and flexible loads for optimization purposes. The UC problem is tackled under uncertainties related to demand and renewable generation capacities. To account for these uncertainties, probability density functions (PDFs) are assigned to the sources of uncertainty, and Monte Carlo simulation (MCS) is employed to select several scenarios with specific probability coefficients. Additionally, two innovative objective functions based on operation cost and emissions are introduced, with each scenario weighted based on its occurrence probability. To assess the performance of the proposed MOGOA-MBES algorithm, simulations are conducted across three scenarios with varying conditions, and the results are compared against those obtained from several multiobjective algorithms. Our findings, supported by optimization results and the S-metric index, demonstrate that the proposed MOGOA-MBES algorithm outperforms other algorithms in terms of reducing operation cost and emissions. Furthermore, the simulation results reveal that uncertainties lead to an increase in cost and emissions, whereas the inclusion of flexible loads and their participation in the UC program can effectively mitigate cost and emission levels.

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Low Carbon Multi-Objective Unit Commitment Integrating Renewable Generations

Cited by 13 publications

References 48 publications

Reinforcement Learning-Based Multi-Objective Optimization for Generation Scheduling in Power Systems

Reinforcement Learning-Based Multi-Objective Optimization for Generation Scheduling in Power Systems

Benefits of Multiobjective Learning in Solar Energy Prediction

Stochastic Unit Commitment Study in a Power System with Flexible Load in Presence of High Penetration Renewable Farms

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