Interconnected-energy hubs robust energy management and scheduling in the presence of electric vehicles considering uncertainties

Poursmaeil, Babak; Najmi, Parisa Hosseinpour; Ravadanegh, Sajad Najafi

doi:10.1016/j.jclepro.2021.128167

Cited by 41 publications

(10 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, the worst-case realization was only developed for the price uncertainty. The uncertainties of electricity market prices, wind, and demands have been studied by the RO also in [86] and [117], with extended affine arithmetic and RO being used respectively. The solution to the RO problem has been developed through an evolutionary algorithm in [85], where the authors consider a combination of the mentioned uncertainty sources.…”

Section: ) Classic Romentioning

confidence: 99%

Operation and Planning of Energy Hubs Under Uncertainty—A Review of Mathematical Optimization Approaches

et al. 2023

View full text Add to dashboard Cite

Co-designing energy systems across multiple energy carriers is increasingly attracting attention of researchers and policy makers, since it is a prominent means of increasing the overall efficiency of the energy sector. Special attention is attributed to the so-called energy hubs, i.e., clusters of energy communities featuring electricity, gas, heat, hydrogen, and also water generation and consumption facilities. Managing an energy hub entails dealing with multiple sources of uncertainty, such as renewable generation, energy demands, wholesale market prices, etc. Such uncertainties call for sophisticated decision-making techniques, with mathematical optimization being the predominant family of decision-making methods proposed in the literature of recent years. In this paper, we summarize, review, and categorize research studies that have applied mathematical optimization approaches towards making operational and planning decisions for energy hubs. Relevant methods include robust optimization, information gap decision theory, stochastic programming, and chance-constrained optimization. The results of the review indicate the increasing adoption of robust and, more recently, hybrid methods to deal with the multi-dimensional uncertainties of energy hubs.

show abstract

Section: ) Classic Romentioning

confidence: 99%

Operation and Planning of Energy Hubs Under Uncertainty—A Review of Mathematical Optimization Approaches

et al. 2023

View full text Add to dashboard Cite

show abstract

“…However, obtaining an accurate distribution is not easy, scenario generation is essential, although deep generative models [9] have also been used for scenario generation in recent years, the challenging model training and high data requirements are also needed. Robust optimization eliminates these steps and directly assumes that uncertain events will occur within a range, thus optimizing for the worst possible scenarios that may occur in the future [10][11][12][13]. Robust optimization ensures robustness by setting uncertainty intervals, which can easily lead to overly conservative decisions.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement learning based two‐timescale energy management for energy hub

Chen,

Mao,

Sha

et al. 2024

IET Renewable Power Gen

View full text Add to dashboard Cite

Maintaining energy balance and economical operation is significant for energy hub (EH) which serves as the central component. Implementing real‐time regulation for heating and cooling equipment within the EH is challenging due to their slow response time in response to the stochastic fluctuation in renewable energy sources and demands while the opposite is true for electric energy storage equipment (EST), a conventional single timescale energy management strategy is no longer sufficient to take into account the operating characteristics of all equipment. With this motivation, this study proposes a deep reinforcement learning based two‐timescale energy management strategy for EH, which controls heating & cooling equipment on a long timescale of 1 h, and EST on a short timescale of 15 min. The actions of the EST are modelled as discrete to reduce the action spaces, and the discrete‐continuous hybrid action sequential TD3 model is proposed to address the problem of handling both discrete and continuous actions in long timescale policy. A joint training approach based on the centralized training framework is proposed to learn multiple levels of policies in parallel. The case studies demonstrate that the proposed strategy reduces the economic cost and carbon emissions by 1%, and 0.5% compared to the single time‐scale strategy respectively.

show abstract

“…Similarly, the grey wolf optimization algorithm is designed to schedule charging and discharging periods by considering low/high electricity pricing time in a RES-ESS integrated system 24 . By adjusting energy demand during low/high tariffs, the optimal scheduling of interconnected multi-energy hubs can be achieved, minimizing total operational costs and carbon emissions 25 . Tooryan et al minimized carbon emissions and increased RES penetration by implementing a PSO algorithm (as a robust meta-heuristic method to schedule BESS) and diesel generators based on the volume of each RES energy generation 26 .…”

Section: Introductionmentioning

confidence: 99%

“…Existing studies 6 – 8 , 25 , 28 focused on shifting the operational time of appliances and RES by considering tariff settings and neglecting the generation and consumption profile. Day and day-ahead scheduling 13 , 14 based on single-objective 26 and multi-objective 11 , 24 optimization functions were developed without considering the appliances’ predicted operational restraints.…”

Section: Introductionmentioning

confidence: 99%

Deep learning based optimal energy management for photovoltaic and battery energy storage integrated home micro-grid system

Alam

Rahman

Ahmed

et al. 2022

Sci Rep

View full text Add to dashboard Cite

The development of the advanced metering infrastructure (AMI) and the application of artificial intelligence (AI) enable electrical systems to actively engage in smart grid systems. Smart homes with energy storage systems (ESS) and renewable energy sources (RES)-known as home microgrids-have become a critical enabling technology for the smart grid. This article proposes a new model for the energy management system of a home microgrid integrated with a battery ESS (BESS). The proposed dynamic model integrates a deep learning (DL)-based predictive model, bidirectional long short-term memory (Bi-LSTM), with an optimization algorithm for optimal energy distribution and scheduling of a BESS-by determining the characteristics of distributed resources, BESS properties, and the user’s lifestyle. The aim is to minimize the per-day electricity cost charged by time-of-use (TOU) pricing while considering the day-basis peak demand penalty. The proposed system also considers the operational constraints of renewable resources, the BESS, and electrical appliances. The simulation results from realistic case studies demonstrate the validation and responsibility of the proposed system in reducing a household’s daily electricity cost.

show abstract

Interconnected-energy hubs robust energy management and scheduling in the presence of electric vehicles considering uncertainties

Cited by 41 publications

References 37 publications

Operation and Planning of Energy Hubs Under Uncertainty—A Review of Mathematical Optimization Approaches

Operation and Planning of Energy Hubs Under Uncertainty—A Review of Mathematical Optimization Approaches

Reinforcement learning based two‐timescale energy management for energy hub

Deep learning based optimal energy management for photovoltaic and battery energy storage integrated home micro-grid system

Contact Info

Product

Resources

About