2021
DOI: 10.35833/mpce.2020.000620
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Optimal Decomposition of Stochastic Dispatch Schedule for Renewable Energy Cluster

Abstract: The correlated renewable energy farms are usually aggregated as a cluster in economic dispatch to relieve computational burden. This strategy can also achieve better performance since the precision of predicting the power generation of a cluster can be higher than those of individual farms. This paper proposes an optimal decomposition method to allocate dispatch schedules among renewable energy farms (REFs) in the cluster under existing stochastic optimization framework. The proposed model takes advantage of p… Show more

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Cited by 12 publications
(5 citation statements)
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“…Here, ξ represents the predicted photovoltaic output vector; P(ξ) represents the probability distribution function of photovoltaic output; E P represents the expected value of the probability distribution function; a T x represents the investment cost of equipment in the IES; b T y + c T ξ represents the operating cost of the second stage. Furthermore, with the help of the uncertain set construction method mentioned in reference [23], the objective function in (18) can be rewritten as (19):…”
Section: Solving Algorithm Of the Two Stage Dro Modelmentioning
confidence: 99%
See 1 more Smart Citation
“…Here, ξ represents the predicted photovoltaic output vector; P(ξ) represents the probability distribution function of photovoltaic output; E P represents the expected value of the probability distribution function; a T x represents the investment cost of equipment in the IES; b T y + c T ξ represents the operating cost of the second stage. Furthermore, with the help of the uncertain set construction method mentioned in reference [23], the objective function in (18) can be rewritten as (19):…”
Section: Solving Algorithm Of the Two Stage Dro Modelmentioning
confidence: 99%
“…However, the accuracy of the prediction curve is often limited by the modeling of probability distribution models. The more precise the uncertain parameter probability distribution model, the higher the complexity of the model, which ultimately leads to difficulty in obtaining results and consequently affects the reliability of stochastic optimization [17,18]. In contrast, robust optimization solutions are reliable and efficient, and can construct corresponding uncertain sets for solving based on the characteristics of uncertain variables, without the need for accurate modeling of the distribution function of uncertain variables [19].…”
Section: Introductionmentioning
confidence: 99%
“…With the depletion of traditional fossil energy and the increasingly severe environmental problems it brings, countries worldwide are gradually falling into the dilemma of traditional fossil energy, and it is imminent to promote the reform of traditional energy structure (Pan et al, 2021a;Bertram et al, 2021;Wu et al, 2022). Countries have promulgated regulations and policies to promote the vigorous development of renewable energy sources such as wind and solar to achieve the goal of carbon neutrality (Holdmann et al, 2019;O'Malley et al, 2020;Yang et al, 2021). The International Energy Agency (IEA) states that renewable energy generation needs to grow at an annual rate of nearly 12% over the 2021-2030 period to put the world on track for net-zero emissions by 2050 (International Energy Agency, 2021).…”
Section: Introductionmentioning
confidence: 99%
“…In reality, fresh energy's geographical and temporal uncertainty is well solved by fuzzy clustering. Wind power has a good association with weather elements like wind speed and wind direction in time and space [6] . The predicted power output must be rectified since there is a defect of prediction error when the influence of external elements is not taken into account, and there is frequently a big departure when experiencing significant changes in the outside environment.…”
Section: Introductionmentioning
confidence: 99%