Optimal power peak shaving using hydropower to complement wind and solar power uncertainty

Liu, Benxi; Lund, Jay R.; Liao, Shengli; Jin, Xiaoyu; Liu, Lingjun; Cheng, Chuntian

doi:10.1016/j.enconman.2020.112628

Cited by 122 publications

(29 citation statements)

References 36 publications

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“…Mirzamohammadi et al [32] also investigated the role of renewable resources uncertainty for energy supply planning for greenhouse farming. Further, the role of uncertainty in wind and solar power was evaluated for power grid peak shaving [33]. Optimal operation of a combined cooling, heating, and power micro-grid system was studied under the impact of wind power output uncertainty using an improved two-stage robust optimization model [34].…”

Section: Literature Reviewmentioning

confidence: 99%

Investigating the Impact of Economic Uncertainty on Optimal Sizing of Grid-Independent Hybrid Renewable Energy Systems

et al. 2021

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One of the many barriers to decarbonization and decentralization of the energy sector in developing countries is the economic uncertainty. As such, this study scrutinizes economics of three grid-independent hybrid renewable-based systems proposed to co-generate electricity and heat for a small-scale load. Accordingly, the under-study systems are simulated and optimized with the aid of HOMER Pro software. Here, a 20-year average value of discount and inflation rates is deemed a benchmark case. The techno-economic-environmental and reliability results suggest a standalone solar/wind/electrolyzer/hydrogen-based fuel cell integrated with a hydrogen-based boiler system is the best alternative. Moreover, to ascertain the impact of economic uncertainty on optimal unit sizing of the nominated model, the fluctuations of the nominal discount rate and inflation, respectively, constitute within the range of 15–20% and 10–26%. The findings of economic uncertainty analysis imply that total net present cost (TNPC) fluctuates around the benchmark value symmetrically between $478,704 and $814,905. Levelized energy cost varies from an amount 69% less than the benchmark value up to two-fold of that. Furthermore, photovoltaic (PV) optimal size starts from a value 23% less than the benchmark case and rises up to 55% more. The corresponding figures for wind turbine (WT) are, respectively, 21% and 29%. Eventually, several practical policies are introduced to cope with economic uncertainty.

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Section: Literature Reviewmentioning

confidence: 99%

Investigating the Impact of Economic Uncertainty on Optimal Sizing of Grid-Independent Hybrid Renewable Energy Systems

et al. 2021

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“…The proposed method is modelled as a mixed-integer linear stochastic program that considers several uncertain parameters, including wind power outputs, prices for energy, balancing, and regulation, and regulation deployments. Liu et al [17] proposed coordinated operation of hydropower and renewable energy in a provincial power grid is explored to alleviate fluctuation and aid peak shaving. This study aggregates wind power plants and solar power plants into a virtual wind power plant and a virtual solar power plant, respectively, and the forecasted error distribution of wind and solar power is analysed with kernel density estimation.…”

Section: Background and Motivationmentioning

confidence: 99%

A new financial loss/gain wind power forecasting method based on deep machine learning algorithm by using energy storage system

Keynia

Memarzadeh

2021

IET Generation Trans & Dist

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Nowadays, with the development of the global economy, traditional non‐renewable energy resources can not only meet the increasing energy demand but also bring severe ecological and environmental problems. Wind power, as one of the most economical types of renewable energies source, has become one of the essential resources for clean energy production. Because of the randomness, intermittency, and fluctuation of natural wind, the output power of wind turbines is greatly affected by wind uncertainty. Therefore, wind power generation forecasting and scheduling may be challenging. In this paper, for the successful and accurate presence of wind power producers in the electricity energy market, a method based on forecasting wind power production, the electricity price, and Financial Loss/Gain (FLG) in coordination with energy storage is proposed. To predict the electricity price, wind power production, and FLG for the next 24 h, the hybrid method based on deep learning time series prediction based on the LSTMs method and input selection based on the MRMI method has been used. For this purpose, first, based on historical data, the wind power producer forecasts the electricity price and wind power production for the next 24 h. According to the same predicted values, initial offers are set for participation in the day‐ahead electricity market. After that, wind power producers modify their wind power production based on the FLG prediction method. Since the FLG signal has highly volatile behaviour, therefore it is not efficient to forecast and apply it directly to the proposed wind power production offers. Therefore, classifying FLG by the FCM method and predicting the FLG class labels is much more helpful in improving the proposed bid of wind power producers to the electricity market. Finally, the wind power producer can improve its profit from participating in the electricity market by interacting with the energy storage unit. The presented numerical results demonstrate the efficiency of the proposed method. For example, if the initial offers are modified based on the FLG method, it has caused the expected profit to improve by 4.44–27.69% in the desired months of the year 2018. Also, in three months, the total profit of the wind power producer and energy storage will be higher than if the profit of the wind power producer participates in the day‐ahead market with 100% accuracy.

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“…Over time, the water resources systems modelers have also broadened the scope of their models beyond water systems and added other components such as food, energy, climate, and ecology (e.g., [144,[171][172][173][174][175][176][177][178][179][180][181][182]). So, their research is organically transitioning into the CHANS space.…”

Section: Is Socio-hydrology Converging To Water Resources Systems or mentioning

confidence: 99%

Socio-Hydrology: A New Understanding to Unite or a New Science to Divide?

Madani

Shafiee-Jood

2020

Water

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The socio-hydrology community has been very successful in promoting the need for taking the human factor into account in the mainstream hydrology literature since 2012. However, the interest in studying and modeling human-water systems is not new and pre-existed the post-2012 socio-hydrology. So, it is critical to ask what socio-hydrology has been able to offer that would have been unachievable using the existing methods, tools, and analysis frameworks. Thus far, the socio-hydrology studies show a strong overlap with what has already been in the literature, especially in the water resources systems and coupled human and natural systems (CHANS) areas. Nevertheless, the work in these areas has been generally dismissed by the socio-hydrology literature. This paper overviews some of the general concerns about originality, practicality, and contributions of socio-hydrology. It is argued that while in theory, a common sense about the need for considering humans as an integral component of water resources systems models can strengthen our coupled human-water systems research, the current approaches and trends in socio-hydrology can make this interest area less inclusive and interdisciplinary.

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Optimal power peak shaving using hydropower to complement wind and solar power uncertainty

Cited by 122 publications

References 36 publications

Investigating the Impact of Economic Uncertainty on Optimal Sizing of Grid-Independent Hybrid Renewable Energy Systems

Investigating the Impact of Economic Uncertainty on Optimal Sizing of Grid-Independent Hybrid Renewable Energy Systems

A new financial loss/gain wind power forecasting method based on deep machine learning algorithm by using energy storage system

Socio-Hydrology: A New Understanding to Unite or a New Science to Divide?

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