A lifetime-extending model-based predictive control for scheduling in concentrating solar power plants

Cojocaru, Emilian Gelu; Vasallo, Manuel Jesús; Bravo, José Manuel; Marín, Diego

doi:10.1109/icit.2018.8352444

Cited by 4 publications

(4 citation statements)

References 22 publications

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“…Therefore, more rigid generation profiles do not significantly worsen the economic profits; in fact, a slight improvement of profits is observed in some cases. When a perfect solar resource forecast is considered (forecast without error), the obtained results are quite similar, which indicates a certain independence from the DNI forecast accuracy. The proposed MPC BR scheduling strategy is also compared with a DAS strategy (without hourly rescheduling). In this case, MPC BR provides a superior performance owing to the rescheduling capacity. As discussed above, the proposed method obtains some results with a slight increment of economic profits with respect to the reference case (MPC without PB protection); thus, it overcomes the previous method presented in the work of Cojocaru et al The possibility to estimate the highest level of cycling penalization, which maintains the economic profits, is also studied and validated.…”

Section: Resultsmentioning

confidence: 74%

“… Having values of

C_{{normalΔ}_{β}} = 0

C_{{normalΔ}_{λ}} = 0

, and C Δ = 0 corresponds to a case with no PB protection. Considering a strategy with

C_{{normalΔ}_{β}} = C_{{normalΔ}_{λ}}

and

C_{{normalΔ}_{λ}} = C_{normalΔ} > 0

, the PB protection term is reduced to

- C_{Δ} \sum_{j = 1}^{N} | Δ P_{e} (j / i) | .

This translates to having the same level of penalization applied to the generation variation during startup, shutdown, or normal operation of the turbine. Similar penalization terms have been used in other works For a different case of interest, with

C_{{normalΔ}_{β}} = 0

C_{{normalΔ}_{λ}} = 0

, and C Δ > 0, the PB protection term is

- C_{Δ} \sum_{j = 1}^{N} | Δ P_{e} (j / i) | (1 - β (j / i) - λ (j / i)) .

In this case, the penalization is not applied during startup and shutdown, considering that they are necessary processes, and the damage due to the intensity of both process is not taken into account. Intermediate values of

…”

Section: Model Predictive Control With Binary‐regularization Approachmentioning

confidence: 99%

“…The study used annual simulations and a perfect forecast scenario, and concluded that the generation variability can be significantly reduced with little impact on economic results. Cojocaru et al proposed to include in the MPC formulation an operational constraint to limit the number of turbine startups, and an ℓ 1 ‐norm term, based on fused lasso, to penalize the changes in the electricity generation. The ℓ 1 ‐norm is an approximation of the sparsity‐inducing ℓ 0 ‐norm and can be used to reduce the variability of the operating point .…”

Section: Introductionmentioning

confidence: 99%

“…This new binary‐regularization term is inspired in trimmed lasso, and combines binary variables with fussed lasso . The key idea is to reduce the changes in the electricity generation while improving the economic results obtained in the work of Cojocaru et al As a means of evaluating the penalization intensity applied to the generation cycling under different power block situations, ie, normal operation, startup, and shutdown, binary variables are used. In order to evaluate the impact of these parameters over the economic profits from energy sales, a simulation case study based on the Spanish day‐ahead market, which carries out a sensibility analysis over the penalization parameters, is included in this paper.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A binary‐regularization‐based model predictive control applied to generation scheduling in concentrating solar power plants

Cojocaru

Bravo

Vasallo

et al. 2019

Optim Control Appl Methods

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Summary This paper proposes the use of model predictive control (MPC) with binary‐regularization to manage the electric power generation problem in concentrating solar power plants with thermal energy storage. The main advantage of the of MPC with binary‐regularization formulation is the inclusion of a power block protection method based on a binary‐regularization term that penalizes power generation variation (also called generation cycling) differently according to the power block situation, ie, normal operation, startup, or shutdown. This distinction simplifies the choice of schedules with reduced variation and high energy sale profits. The interest in this reduction is the achievement of a higher lifetime of the power block elements, lower maintenance costs, and easier plant operability. A benefit of the generation scheduling based on MPC is the capacity of rescheduling the power generation at regular periods, taking advantage of the most recent energy prices and weather forecast, and of the plant's current state. An interesting question is if the proposed protection mechanism affects the economic results of the MPC black strategy. In this regard, an economic study based on a realistic simulation of a 50 MW parabolic trough collector‐based concentrating solar power plant with thermal energy storage, under the assumption of participation in the Spanish day‐ahead energy market scenario, is included. Realistic values for actual and forecasted solar resource and for energy price are used, and for penalties for deviation from the committed generation schedule. The economic study shows that the proposed scheduling method provides an important reduction of the generation cycling without decreasing energy sales profits. Another advantage of the proposed method is the possibility of estimating the highest level of power block protection, which maintains the profits by means of historical data, which favors its practical implementation.

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