Storage sizing for grid connected hybrid wind and storage power plants taking into account forecast errors autocorrelation

Michiorri, Andrea; Lugaro, Jesus; Siebert, Nils; Girard, Robin; Kariniotakis, George

doi:10.1016/j.renene.2017.10.070

Cited by 24 publications

(12 citation statements)

References 38 publications

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“…ii. Probabilistic modeling of errors: sophisticated statistical methods such as copula functions [16], Markov model [17], enhanced Markov chain model [18] and kernel density estimation [19] are utilized to model the time series forecast.…”

Section: Problem Statement and Literature Reviewmentioning

confidence: 99%

Corrective receding horizon EV charge scheduling using short-term solar forecasting

et al. 2019

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Forecast errors can cause sub-optimal solutions in resource planning optimization, yet they are usually modeled simplistically by statistical models, causing unrealistic impacts on the optimal solutions. In this paper, realistic forecast errors are prescribed, and a corrective approach is proposed to mitigate the negative effects of day-ahead persistence forecast error by short-term forecasts from a state-of-the-art sky imager system. These forecasts preserve the spatiotemporal dependence structure of forecast errors avoiding statistical approximations. The performance of the proposed algorithm is tested on a receding horizon quadratic program developed for valley filling the midday net load depression through electric vehicle charging. Throughout one month of simulations the ability to flatten net load is assessed under practical forecast accuracy levels achievable from persistence, sky imager and perfect forecasts. Compared to using day-ahead persistence solar forecasts, the proposed corrective approach using sky imager forecasts delivers a 25% reduction in the standard deviation of the daily net load. It is demonstrated that correcting day-ahead forecasts in real time with more accurate short-term forecasts benefits the valley filling solution.

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Section: Problem Statement and Literature Reviewmentioning

confidence: 99%

Corrective receding horizon EV charge scheduling using short-term solar forecasting

et al. 2019

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“…It can be used to store electricity in hours of high production and inject electricity in the grid later on. The performance of the operational management can be therefore improved by considering simple charge-discharge plans based on short-term forecasts of the renewable production [1]. For instance, optimal management of wind farms associated with hydropower pumped storage showing economic benefit and increasing the controllability have been studied in [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Optimal Bidding in Wind Farm Management

Bensoussan¹,

Brouste²

2020

Modeling, Simulation and Optimization of Wind Farms and Hybrid Systems

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We study the problem of wind farm management, in which the manager commits himself to deliver energy in some future time. He reduces the consequences of uncertainty by using a storage facility (a battery, for instance). We consider a simplified model in discrete time, in which the commitment is for the next period. We solve an optimal control problem to define the optimal bidding decision. Application to a real dataset is done, and the optimal size of the battery (or the overnight costs) for the wind farm is determined. We then describe a continuous time version involving a delay between the time of decision and the implementation.

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“…Focused on wind power consumption, three kinds of research trends have been studied. The first tries to transmit wind power to load centers with power grid by decreasing fluctuation of wind output and wind farm production prediction error, as well as increasing deliverability of transmission‐constrained power . The second refers to the integration of wind power with other renewable energy resources, such as solar power, into the microgrid as an integrated power system.…”

Section: Introductionmentioning

confidence: 99%

Collaboration strategy and optimization model of wind farm‐hybrid energy storage system for mitigating wind curtailment

Liu

Wei

Huang³

et al. 2019

Energy Science & Engineering

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Over the past years, wind energy has been considered as a promising solution for clean and sustainable energy development, but wind curtailment remains a challenge to wind power development. On this basis, utilization of non-grid-connected wind power becomes crucial and necessary as it can mitigate wind curtailment and improve energy efficiency. This paper proposes the collaboration strategy and optimization model of wind farm-hybrid energy storage system (WF-HESS) for nongrid-connected wind power based on battery and superconducting magnetic energy storage (SMES) whose combination can effectively cope with fluctuation and intermittence of wind input. The optimization problem is simultaneously investigated by the minimization of total cost, wind curtailment magnitude, and loss of power supply probability (LPSP). The multi-objective particle swarm optimization (MOPSO) is introduced to find available solutions, and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is applied to determine the optimal one.Results are obtained for a case study, and the effectiveness and the feasibility of proposed model are verified by a comparative analysis and a sensitivity analysis.Results analysis and discussion show that the WF-HESS model and the application of HESS have important influence on promoting utilization of non-grid-connected wind power and mitigating wind curtailment. K E Y W O R D Shybrid energy storage system, multi-objective optimization, multi-objective particle swarm optimization, non-grid-connected wind power, wind curtailment

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Storage sizing for grid connected hybrid wind and storage power plants taking into account forecast errors autocorrelation

Cited by 24 publications

References 38 publications

Corrective receding horizon EV charge scheduling using short-term solar forecasting

Corrective receding horizon EV charge scheduling using short-term solar forecasting

Optimal Bidding in Wind Farm Management

Collaboration strategy and optimization model of wind farm‐hybrid energy storage system for mitigating wind curtailment

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