A MPC approach for optimal generation scheduling in CSP plants

Vasallo, Manuel Jesús; Bravo, José Manuel

doi:10.1016/j.apenergy.2015.12.092

Cited by 62 publications

(36 citation statements)

References 31 publications

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“…This control approach is based on a sliding‐window strategy where a cost function is optimized over a moving time horizon, allowing real‐time optimization. Vasallo and Bravo suggested to use a generation rescheduling mechanism based on an MPC approach in CSP plants with TES. Mixed‐integer programming (MIP) was used as modeling tool.…”

Section: Introductionmentioning

confidence: 99%

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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Section: Introductionmentioning

confidence: 99%

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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“…In recent years, solar energy has become the second-largest energy source after wind energy among the renewable energy sources that are used for electricity production [1]. The concentrating solar power (CSP), which uses either organic oil or molten salt as its heat transfer fluid (HTF) to absorb and transfer solar energy, is currently the most commercially attractive solar thermal-based power generation technology [2].…”

Section: Introductionmentioning

confidence: 99%

Transient Analysis and Execution-Level Power Tracking Control of the Concentrating Solar Thermal Power Plant

Liang

2019

Energies

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Concentrating solar power (CSP) is a promising technology for exploiting solar energy. A major advantage of CSP plants lies in their capability of integrating with thermal energy storage; hence, they can have a similar operability to that of fossil-fired power plants, i.e., their power output can be adjusted as required. For this reason, the power output of such CSP plants is generally scheduled to maximize the operating revenue by participating in electric markets, which can result in frequent changes in the power reference signal and introduces challenges to real-time power tracking. To address this issue, this paper systematically studies the execution-level power tracking control strategy of an CSP plant, primarily aiming at coordinating the control of the sluggish steam generator (including the economizer, the boiler, and the superheater) and the fast steam turbine. The governing equations of the key energy conversion processes in the CSP plant are first presented and used as the simulation platform. Then, the transient behavior of the CSP plant is analyzed to gain an insight into the system dynamic characteristics and control difficulties. Then, based on the step-response data, the transfer functions of the CSP plant are identified, which form the prediction model of the model predictive controller. Finally, two control strategies are studied through simulation experiments: (1) the heuristic PI control with two operation modes, which can be conveniently implemented but cannot coordinate the control of the power tracking speed and the main steam parameters, and (2) advanced model predictive control (MPC), which overcomes the shortcoming of PI (Proportional-Integral) control and can significantly improve the control performance.

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“…4 Recent literature considers direct participation of CSP systems in wholesale electricity markets. Vasallo and Bravo 16,17 propose a two-model approach to address this issue; here, a MILP is solved to determine the optimal generation schedule which is then simulated with a high-fidelity dynamic model implemented in SAM. Integer variables are used to model transitions between different operating modes (e.g., generating mode, warm, off, antifreeze, and etc.).…”

Section: Introductionmentioning

confidence: 99%

“…[12][13][14][15] Combining scheduling and control (dynamic optimization) models results in nonconvex mixed integer nonlinear programs (MINLPs) that cannot be solved by offthe-shelf solvers. Vasallo and Bravo 16,17 propose a two-model approach to address this issue; here, a MILP is solved to determine the optimal generation schedule which is then simulated with a high-fidelity dynamic model implemented in SAM. 10 The high-fidelity simulation results are then used to tune the optimization model.…”

Section: Introductionmentioning

confidence: 99%

A decomposition algorithm for simultaneous scheduling and control of CSP systems

2018

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We present a decomposition algorithm to perform simultaneous scheduling and control decisions in concentrated solar power (CSP) systems. Our algorithm is motivated by the need to determine optimal market participation strategies at multiple timescales. The decomposition scheme uses physical insights to create surrogate linear models that are embedded within a mixed‐integer linear scheduling layer to perform discrete (operational mode) decisions. The schedules are then validated for physical feasibility in a dynamic optimization layer that uses a continuous full‐resolution CSP model. The dynamic optimization layer updates the physical variables of the surrogate models to refine schedules. We demonstrate that performing this procedure recursively provides high‐quality solutions of the simultaneous scheduling and control problem. We exploit these capabilities to analyze different market participation strategies and to explore the influence of key design variables on revenue. Our results also indicate that using scheduling algorithms that neglect detailed dynamics significantly decreases market revenues. © 2018 American Institute of Chemical Engineers AIChE J, 64: 2408–2417, 2018

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A MPC approach for optimal generation scheduling in CSP plants

Cited by 62 publications

References 31 publications

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

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

Transient Analysis and Execution-Level Power Tracking Control of the Concentrating Solar Thermal Power Plant

A decomposition algorithm for simultaneous scheduling and control of CSP systems

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