Short-term optimal operation of hydro-wind-solar hybrid system with improved generative adversarial networks

Hu, Wei; Zhang, Hongxuan; Dong, Yu; Wang, Yiting; Dong, Lei; Xiao, Ming

doi:10.1016/j.apenergy.2019.04.090

Cited by 113 publications

(26 citation statements)

References 46 publications

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“…There are multiple works that propose different versions of the different DGMs reviewed above for energy scenario generation. Applications of GANs and WGANs include wind power generation [17], [19], PV power generation [16], [18], [20]- [22], and residential loads [25]. VAEs were applied to learn the distributions of PV and wind power generation [15], concentrated solar power [23], and electric vehicle power demand [24].…”

Section: Dgm-based Scenario Generationmentioning

confidence: 99%

“…The PSD of any period longer than the scenario length describes the concatenation of scenarios, i.e., its use is inconsequential to the aim of validation. However, most authors present the PSD of longer periods [16], [18], [23]. For instance, the authors in [16] generate 24 h scenarios in 5 min resolution, but present the PSD over periods between 6 d and 1 h, which neglects short periods reflecting the short-term behavior.…”

Section: Power Spectral Densitymentioning

confidence: 99%

“…Following the standard literature approach, we concatenate the generated scenarios to form a single continuous time series and then calculate the PSD using Welch's method [16], [18], [23]. The results for PV capacity factor, wind capacity factor, and intra-day prices are shown in Figure 7.…”

Section: Power Spectral Densitymentioning

confidence: 99%

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Validation Methods for Energy Time Series Scenarios From Deep Generative Models

et al. 2022

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The design and operation of modern energy systems are heavily influenced by time-dependent and uncertain parameters, e.g., renewable electricity generation, load-demand, and electricity prices. These are typically represented by a set of discrete realizations known as scenarios. A popular scenario generation approach uses deep generative models (DGM) that allow scenario generation without prior assumptions about the data distribution. However, the validation of generated scenarios is difficult, and a comprehensive discussion about appropriate validation methods is currently lacking. To start this discussion, we provide a critical assessment of the currently used validation methods in the energy scenario generation literature. In particular, we assess validation methods based on probability density, auto-correlation, and power spectral density. Furthermore, we propose using the multifractal detrended fluctuation analysis (MFDFA) as an additional validation method for non-trivial features like peaks, bursts, and plateaus. As representative examples, we train generative adversarial networks (GANs), Wasserstein GANs (WGANs), and variational autoencoders (VAEs) on two renewable power generation time series (photovoltaic and wind from Germany in 2013 to 2015) and an intra-day electricity price time series form the European Energy Exchange in 2017 to 2019. We apply the four validation methods to both the historical and the generated data and discuss the interpretation of validation results as well as common mistakes, pitfalls, and limitations of the validation methods. Our assessment shows that no single method sufficiently characterizes a scenario but ideally validation should include multiple methods and be interpreted carefully in the context of scenarios over short time periods.

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Section: Dgm-based Scenario Generationmentioning

confidence: 99%

Section: Power Spectral Densitymentioning

confidence: 99%

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Validation Methods for Energy Time Series Scenarios From Deep Generative Models

et al. 2022

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“…In [23], a variational automatic encoder composed of deep convolution networks is proposed to generate load profiles of EVs. In [24], the variational automatic encoder is designed to simulate a large number of times series for wind and photovoltaic powers. The GAN generates data by constructing an antagonistic generator and discriminator.…”

Section: B Literature Reviewmentioning

confidence: 99%

Modeling Daily Load Profiles of Distribution Network for Scenario Generation Using Flow-Based Generative Network

Liao

Wang

et al. 2020

IEEE Access

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The daily load profiles modeling is of great significance for the economic operation and stability analysis of the distribution network. In this paper, a flow-based generative network is proposed to model daily load profiles of the distribution network. Firstly, the real samples are used to train a series of reversible functions that map the probability distribution of real samples to the prior distribution. Then, the new daily load profiles are generated by taking the random number obeying the Gaussian distribution as the input data of these reversible functions. Compared with existing methods such as explicit density models, the proposed approach does not need to assume the probability distribution of real samples, and can be used to model different loads only by adjusting the structure and parameters. The simulation results show that the proposed approach not only fits the probability distribution of real samples well, but also accurately captures the spatial-temporal correlation of daily load profiles. The daily load profiles with specific characteristics can be obtained by simply classification.INDEX TERMS Daily load profiles, distribution network, generative network. BIRGITTE BAK-JENSEN (Senior Member, IEEE) received the M.Sc. degree in electrical engineering and the Ph.D. degree in modeling of high voltage components from the

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“…Due to the above merits, a great deal of attention has been paid to the popular MILP-based methods in practical problems [34][35][36][37]. For instance, the MILP models are developed to determine the generation of head-sensitive reservoir and distribution networks [38][39][40]; the MILP model is used for the short-term operation of hydro-wind-solar hybrid system [41]; the MILP procedure is developed for the analysis of electric grid security under a disruptive threat [42]; a mixed-integer linear framework is developed for robust hydrothermal unit commitment [43]; the MILP model is developed for day-ahead hydro-thermal self-scheduling considering price uncertainty and forced outage rate [44].…”

Section: Introductionmentioning

confidence: 99%

Mixed Integer Linear Programming Model for Peak Operation of Gas-Fired Generating Units with Disjoint-Prohibited Operating Zones

et al. 2019

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Due to booming economic development over the past decades, energy demands in most of China’s provincial power grids have increased sharply, and it has become challenging to guarantee the energy balance at peak periods. In many provincial electric systems of China, gas-fired generators are one of the most important peaking power sources to respond the load change at peak periods. To meet this practical necessity, a novel mixed integer linear programming model is proposed in this paper for the peak operation of gas-fired generating units with disjoint-prohibited operating zones. In the developed model, the objective function is chosen to minimize the peak-valley difference of the remaining load series that is obtained by subtracting the total generation of all the gas-fired units from the original load curve. The real-world simulations in several cases show that the developed model is able to generate satisfying scheduling results by reasonably allocating the power outputs of all the gas-fired generators in the scheduling horizon. Then, the management implications obtained lie in the fact that it is necessary to increase the share of peak power sources in the mid- to long-term planning of an electrical power system; and in the daily operation of the power grid, greater flexibility should be given to the gas-fired units to reduce peak pressure.

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Short-term optimal operation of hydro-wind-solar hybrid system with improved generative adversarial networks

Cited by 113 publications

References 46 publications

Validation Methods for Energy Time Series Scenarios From Deep Generative Models

Validation Methods for Energy Time Series Scenarios From Deep Generative Models

Modeling Daily Load Profiles of Distribution Network for Scenario Generation Using Flow-Based Generative Network

Mixed Integer Linear Programming Model for Peak Operation of Gas-Fired Generating Units with Disjoint-Prohibited Operating Zones

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