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M. Wervers
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Economic Model Predictive Control for HVAC Systems
Under day-ahead electricity prices and a monthly peak demand charge
Congestion on the electricity grid is a growing issue in the Netherlands. To reduce stress on the grid, electricity providers are introducing new pricing structures. This thesis considers a pricing model that combines day ahead electricity prices with an added monthly peak demand charge. The challenge is that classical heating, ventilation, and air-conditioning (HVAC) control strategies are not responsive to these pricing mechanisms, leading to increased operational costs.
This project focuses on a heat pump-based floor heating HVAC system designed to maintain thermal comfort in a small office environment. Economic model predictive control (EMPC) can leverage the building’s thermal mass to exploit variations in time-of-use (TOU) pricing. However, the monthly peak demand charge complicates the control problem, as its long-term effect is difficult to capture with the relatively short prediction horizons typical of MPC. To address this limited horizon, a method is proposed that artificially extends the prediction horizon to better account for long-term cost associated with the peak demand charge.
This artificially extended horizon is enabled by leveraging historical data for both the disturbance and electricity prices, allowing it to be approximated offline as a terminal cost function for the MPC. The proposed method, incorporating this artificial horizon extension into the MPC formulation, is compared to alternative strategies that address the peak demand charge by scaling it down within the cost function based on the length of the prediction horizon.
All methods are evaluated using information on electricity prices and disturbances that would realistically be available in practice. Results show that the MPC approach incorporating a terminal cost function economically outperforms methods that apply aggressive down scaling of the peak demand charge. However, similar economic performance is observed when compared to a more conservative scaling approach. Highlighting how conservatism with respect the peak demand is beneficial when operating under uncertainty
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This project focuses on a heat pump-based floor heating HVAC system designed to maintain thermal comfort in a small office environment. Economic model predictive control (EMPC) can leverage the building’s thermal mass to exploit variations in time-of-use (TOU) pricing. However, the monthly peak demand charge complicates the control problem, as its long-term effect is difficult to capture with the relatively short prediction horizons typical of MPC. To address this limited horizon, a method is proposed that artificially extends the prediction horizon to better account for long-term cost associated with the peak demand charge.
This artificially extended horizon is enabled by leveraging historical data for both the disturbance and electricity prices, allowing it to be approximated offline as a terminal cost function for the MPC. The proposed method, incorporating this artificial horizon extension into the MPC formulation, is compared to alternative strategies that address the peak demand charge by scaling it down within the cost function based on the length of the prediction horizon.
All methods are evaluated using information on electricity prices and disturbances that would realistically be available in practice. Results show that the MPC approach incorporating a terminal cost function economically outperforms methods that apply aggressive down scaling of the peak demand charge. However, similar economic performance is observed when compared to a more conservative scaling approach. Highlighting how conservatism with respect the peak demand is beneficial when operating under uncertainty
...
Congestion on the electricity grid is a growing issue in the Netherlands. To reduce stress on the grid, electricity providers are introducing new pricing structures. This thesis considers a pricing model that combines day ahead electricity prices with an added monthly peak demand charge. The challenge is that classical heating, ventilation, and air-conditioning (HVAC) control strategies are not responsive to these pricing mechanisms, leading to increased operational costs.
This project focuses on a heat pump-based floor heating HVAC system designed to maintain thermal comfort in a small office environment. Economic model predictive control (EMPC) can leverage the building’s thermal mass to exploit variations in time-of-use (TOU) pricing. However, the monthly peak demand charge complicates the control problem, as its long-term effect is difficult to capture with the relatively short prediction horizons typical of MPC. To address this limited horizon, a method is proposed that artificially extends the prediction horizon to better account for long-term cost associated with the peak demand charge.
This artificially extended horizon is enabled by leveraging historical data for both the disturbance and electricity prices, allowing it to be approximated offline as a terminal cost function for the MPC. The proposed method, incorporating this artificial horizon extension into the MPC formulation, is compared to alternative strategies that address the peak demand charge by scaling it down within the cost function based on the length of the prediction horizon.
All methods are evaluated using information on electricity prices and disturbances that would realistically be available in practice. Results show that the MPC approach incorporating a terminal cost function economically outperforms methods that apply aggressive down scaling of the peak demand charge. However, similar economic performance is observed when compared to a more conservative scaling approach. Highlighting how conservatism with respect the peak demand is beneficial when operating under uncertainty
This project focuses on a heat pump-based floor heating HVAC system designed to maintain thermal comfort in a small office environment. Economic model predictive control (EMPC) can leverage the building’s thermal mass to exploit variations in time-of-use (TOU) pricing. However, the monthly peak demand charge complicates the control problem, as its long-term effect is difficult to capture with the relatively short prediction horizons typical of MPC. To address this limited horizon, a method is proposed that artificially extends the prediction horizon to better account for long-term cost associated with the peak demand charge.
This artificially extended horizon is enabled by leveraging historical data for both the disturbance and electricity prices, allowing it to be approximated offline as a terminal cost function for the MPC. The proposed method, incorporating this artificial horizon extension into the MPC formulation, is compared to alternative strategies that address the peak demand charge by scaling it down within the cost function based on the length of the prediction horizon.
All methods are evaluated using information on electricity prices and disturbances that would realistically be available in practice. Results show that the MPC approach incorporating a terminal cost function economically outperforms methods that apply aggressive down scaling of the peak demand charge. However, similar economic performance is observed when compared to a more conservative scaling approach. Highlighting how conservatism with respect the peak demand is beneficial when operating under uncertainty