A Unified Trading Model Based on Robust Optimization for Day-Ahead and Real-Time Markets with Wind Power Integration

Jiang, Yuewen; Chen, Meisen; You, Shi

doi:10.3390/en10040554

Cited by 9 publications

(1 citation statement)

References 24 publications

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“…Since renewable energies take a growing share in the energy resource portfolio, shortterm forecasts considering the effect of intermittent resources on both the price and imbalances of the power market as in Jiang et al [37], Qiu et al [38] and Gligoric et al [39]. These researches either try to reduce the prediction errors for the day ahead analysis or introducing stochasticity and fuzziness.…”

Section: Imbalances In State Regulated Power Markets: Case Of Turkeymentioning

confidence: 99%

Predicting Power Deviation in the Turkish Power Market Based on Adaptive Factor Impacts

Guven

Ozozen²,

Kayakutlu

et al. 2021

IFIP Advances in Information and Communication Technology

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Energy market models are generally focused on energy balancing using the optimum energy mix. In countries where the energy markets are not fully liberalised, the State Regulators reflect any cost of being off-balance on the utility companies and this affects the consumers as well. The right short term prediction of the market trends is beneficial both to optimise the physical energy flow and commercial revenue balance for suppliers and utility companies. This study is aimed to predict the sign trends in the power market by selecting the influencing factors adaptive to the conditions of the day ahead, 10 hours, 5 hours, 2 hours and 1 hour before the electricity balance is active. There are numerous factors consisting of weather conditions, resource costs, operation costs, renewable energy conditions, regulations, etc. with a considerable impact on the predictions. The contribution of this paper is to choose the factors with the highest impacts using the Genetic Algorithm (GA) with Akaike Information Criteria (AIC), which are then used as input of a Recursive Neural Network (RNN) model for forecasting the deviation trends. The proposed hybrid method does not only reduce the prediction errors but also avoid dependency on expert knowledge. Hence this paper will allow both the market regulator and the suppliers to take precautions based on a confident prediction.

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Section: Imbalances In State Regulated Power Markets: Case Of Turkeymentioning

confidence: 99%

Predicting Power Deviation in the Turkish Power Market Based on Adaptive Factor Impacts

Guven

Ozozen²,

Kayakutlu

et al. 2021

IFIP Advances in Information and Communication Technology

View full text Add to dashboard Cite

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Interval optimization of the day-ahead clearing schedule considering the real-time imbalance power with wind power integration

Jiang

Chen

Bu-ying

2018

Int Trans Electr Energ Syst

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Summary Large‐scale wind power penetration impacts the electricity market in many aspects, especially the day‐ahead clearing schedule, due to the uncertainty and low predictability of wind power. Based on forecast intervals of wind power and absorption of wind power with a preference, the day‐ahead clearing model of energy and reserve is built and contains 2 objective functions: the minimum clearing cost and real‐time imbalance power. The constraints for the clearing schedule and for meeting the uncertainty of wind power in forecast intervals are included. To supply market operators with upper and lower boundary information, the interval optimization model is converted into double‐layer nonlinear pessimistic and optimistic models. The improved multiobjective quantum particle swarm optimization (MOQPSO) with a “straight Pareto front” is developed to approximate the real, smooth, and uniform Pareto front. The day‐ahead clearing schedule and corresponding reserve capacity are analyzed by testing IEEE30‐bus system. The computational times of the improved MOQPSO and general MOQPSO are nearly the same, but the Pareto front is remarkably reformative. Furthermore, the upward/downward energy price in the real‐time market can considerably impact the day‐ahead clearing schedule.

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A survey and comparison of leading-edge uncertainty handling methods for power grid modernization

Rahim

Siano

2022

Expert Systems with Applications

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A Unified Trading Model Based on Robust Optimization for Day-Ahead and Real-Time Markets with Wind Power Integration

Cited by 9 publications

References 24 publications

Predicting Power Deviation in the Turkish Power Market Based on Adaptive Factor Impacts

Predicting Power Deviation in the Turkish Power Market Based on Adaptive Factor Impacts

Interval optimization of the day-ahead clearing schedule considering the real-time imbalance power with wind power integration

A survey and comparison of leading-edge uncertainty handling methods for power grid modernization

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