A probabilistic economic dispatch model and methodology considering renewable energy, demand and generator uncertainties

Arriagada, Eduardo; López, E.; López, Miguel; Blasco-Gimenez, R.; Roa, Claudio; Poloujadoff, M.

doi:10.1016/j.epsr.2014.11.018

Cited by 37 publications

(17 citation statements)

References 29 publications

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“…The presence of increased penetration of Renewable Energy Sources (RES) in the power system creates many technological challenges for the power companies owing to the need for improved system control to maintain the power quality to consumers [3]. The mix of conventional and RES must work in tandem to maintain the power generation at the required level.…”

Section: Introductionmentioning

confidence: 99%

Multi-Agent Systems for Resource Allocation and Scheduling in a Smart Grid

Nair

Hossen

Campion

et al. 2018

Technol Econ Smart Grids Sustain Energy

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With the increasing integration of Distributed Energy Resources (DER) in the power grid, a decentralized approach becomes essential for scheduling and allocation of resources in a smart grid. Economic Dispatch (ED) and Unit Commitment (UC) are the two major resource allocation problems that play critical role in the safe and stable operation of a grid system. The uncertainty associated with renewable energy sources have made the resource allocation problems even more challenging for grid operators. The future grid will have a higher generation mix of renewable energy sources and a large load of Electrical vehicles, with the possibility of bi-directional power flow. This complex smart grid system necessitates the development of a decentralized approach to resource allocation problem, which allows inter-node communication and decision making. Multi-agent systems (MAS) is a promising platform to decentralize the traditional centralized resource allocation aspects of smart grid. This paper presents a comprehensive literature review on the application of MAS to Economic Dispatch (ED) and Unit Commitment (UC) in smart grids.

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Section: Introductionmentioning

confidence: 99%

Multi-Agent Systems for Resource Allocation and Scheduling in a Smart Grid

Nair

Hossen

Campion

et al. 2018

Technol Econ Smart Grids Sustain Energy

View full text Add to dashboard Cite

show abstract

“…The simulations based on real household production and consumption data present achievable performance improvements. Arriagada et al [31] modelled the probability distributions of system marginal price, thermal (fuel based), solar, and wind power generation. The model was applied to a case study of the Northern Chilean electrical system and showed to be helpful for multi decision-making regarding renewable energy market effects.…”

Section: Weather Forecasts: Predicting Res Resourcementioning

confidence: 99%

Artificial Learning Dispatch Planning with Probabilistic Forecasts: Using Uncertainties as an Asset

2020

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Weather forecast uncertainty is a key element for energy market volatility. By intelligently considering uncertainties on the schedule development, renewable energy systems with storage could improve dispatching accuracy, and therefore, effectively participate in electricity wholesale markets. Deterministic forecasts have been traditionally used to support dispatch planning, representing reduced or no uncertainty information about the future weather. Aiming at better representing the uncertainties involved, probabilistic forecasts have been developed to increase forecasting accuracy. For the dispatch planning, this can highly influence the development of a more precise schedule. This work extends a dispatch planning method to the use of probabilistic weather forecasts. The underlying method used a schedule optimizer coupled to a post-processing machine learning algorithm. This machine learning algorithm was adapted to include probabilistic forecasts, considering their additional information on uncertainties. This post-processing applied a calibration of the planned schedule considering the knowledge about uncertainties obtained from similar past situations. Simulations performed with a concentrated solar power plant model following the proposed strategy demonstrated promising financial improvement and relevant potential in dealing with uncertainties. Results especially show that information included in probabilistic forecasts can increase financial revenues up to 15% (in comparison to a persistence solar driven approach) if processed in a suitable way.

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“…As specified by the "Implementation Rules for the Management of Auxiliary Services for Grid-Connected Power Plants in North China", the reduced amount of electricity for the deep peak-shaving units will be compensated by the government (only for peak-shaving level deeper than 50%). The peak shaving compensation can be calculated as follows (19) where E R is the power generation under the condition all units do not engage in deep peak shaving; E D is the power generation during DPSW and DPSC stages; S com is the compensation price and settled as S com = 37.2 $/(MWh) according to [35]. Then, the marginal price with compensation could be refined as…”

Section: Economy Of Thermal Power Plants In Peak Shavingmentioning

confidence: 99%

Multi-Angle Economic Analysis of Coal-Fired Units with Plasma Ignition and Oil Injection during Deep Peak Shaving in China

Xia

2019

Applied Sciences

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What China committed in the Paris Agreement encourages the penetration of renewable energy in power grid. To consume more renewable energy, coal-fired units undertake the most part of peak shaving task and are usually operated at a low-load level during off-peak hours. However, deep peak shaving has harmed the benefits of thermal power plants and also brought about environmental problems. To improve the peak-shaving capacity and operation efficiency of coal-fired units, the government encourages the flexibility retrofits for coal-fired units. In this paper, peak-shaving related cost functions are proposed for the multi-angle economic analysis of coal-fired unit with plasma ignition (UPI) and oil injection (UOI), respectively. First, the operation characteristic is analyzed for three stages of peak shaving, and then the peak-shaving costs related to these three stages are proposed in terms of the coal consumption cost, wear-and-tear cost, combustion-supporting cost, and environmental cost. Afterwards, a peak-shaving cost-based economic dispatch model is presented with consideration of the curtailed wind penalty, and an environmental efficiency index is defined to evaluate the environmental benefits. Finally, in the case study, quantitative economy analysis is performed from the aspects of thermal power plants, wind power plants, and the environment separately, and the simulation results indicate that UPI has better peak-shaving economy and environmental efficiency than UOI.

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A probabilistic economic dispatch model and methodology considering renewable energy, demand and generator uncertainties

Cited by 37 publications

References 29 publications

Multi-Agent Systems for Resource Allocation and Scheduling in a Smart Grid

Multi-Agent Systems for Resource Allocation and Scheduling in a Smart Grid

Artificial Learning Dispatch Planning with Probabilistic Forecasts: Using Uncertainties as an Asset

Multi-Angle Economic Analysis of Coal-Fired Units with Plasma Ignition and Oil Injection during Deep Peak Shaving in China

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