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A. Riccardi
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Modern power systems are being fundamentally reshaped by the increasing integration of renewable energy sources, such as solar and wind power generation. Their inherent variability poses significant challenges for real-time grid control. In particular, advanced control strategies, including model predictive control, require multi-step forecasts at temporal resolutions relevant to the specific control application, which in this work is on the order of seconds. However, commonly available meteorological datasets are often temporally sparse, creating a pronounced mismatch between data availability and control requirements. To address this challenge, this thesis investigates the use of physics-informed neural networks to forecast photovoltaic (PV) power generation in spatially distributed PV systems at temporal resolutions on the order of seconds under temporal data sparsity.
The proposed PINN incorporates partial physical knowledge of atmospheric processes through a spatiotemporal cloud motion equation, enabling physically plausible generalization to unseen time steps and conditions. To generate multi-step forecasts at temporal resolutions on the order of seconds, a recursive forecasting framework is developed. In addition, an improved training strategy is proposed in which the model is trained using its own recursively generated forecasts, thereby reducing the mismatch between training and inference. Finally, a novel correlation-based collocation point sampling strategy is developed to generate physically plausible and statistically representative collocation points, thereby supporting effective physics-based regularization.
The proposed methods are evaluated through two complementary case studies. In a case study based on a selected region in France, the PINN with the improved training strategy demonstrates a clear advantage under challenging atmospheric conditions compared to a persistence model, which assumes constant behavior over the forecast horizon. It also achieves superior data efficiency relative to purely data-driven neural networks and improved computational efficiency over a physics-only model during inference. The proposed collocation point sampling strategy also consistently outperforms uniform sampling.
In addition, a real-world case study based on a measurement site in Hawaii validates the forecasting framework under a realistic PV deployment scenario in which available data are not only temporally sparse but also satellite-derived. Because such data provide only an approximate, temporally and spatially smoothed representation of the true atmospheric conditions affecting the PV system, this setting introduces an additional data-reality mismatch. Nevertheless, the framework remains capable of producing meaningful forecasts at temporal resolutions on the order of seconds, establishing its practical applicability for PV systems worldwide without local measurement infrastructure.
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The proposed PINN incorporates partial physical knowledge of atmospheric processes through a spatiotemporal cloud motion equation, enabling physically plausible generalization to unseen time steps and conditions. To generate multi-step forecasts at temporal resolutions on the order of seconds, a recursive forecasting framework is developed. In addition, an improved training strategy is proposed in which the model is trained using its own recursively generated forecasts, thereby reducing the mismatch between training and inference. Finally, a novel correlation-based collocation point sampling strategy is developed to generate physically plausible and statistically representative collocation points, thereby supporting effective physics-based regularization.
The proposed methods are evaluated through two complementary case studies. In a case study based on a selected region in France, the PINN with the improved training strategy demonstrates a clear advantage under challenging atmospheric conditions compared to a persistence model, which assumes constant behavior over the forecast horizon. It also achieves superior data efficiency relative to purely data-driven neural networks and improved computational efficiency over a physics-only model during inference. The proposed collocation point sampling strategy also consistently outperforms uniform sampling.
In addition, a real-world case study based on a measurement site in Hawaii validates the forecasting framework under a realistic PV deployment scenario in which available data are not only temporally sparse but also satellite-derived. Because such data provide only an approximate, temporally and spatially smoothed representation of the true atmospheric conditions affecting the PV system, this setting introduces an additional data-reality mismatch. Nevertheless, the framework remains capable of producing meaningful forecasts at temporal resolutions on the order of seconds, establishing its practical applicability for PV systems worldwide without local measurement infrastructure.
...
Modern power systems are being fundamentally reshaped by the increasing integration of renewable energy sources, such as solar and wind power generation. Their inherent variability poses significant challenges for real-time grid control. In particular, advanced control strategies, including model predictive control, require multi-step forecasts at temporal resolutions relevant to the specific control application, which in this work is on the order of seconds. However, commonly available meteorological datasets are often temporally sparse, creating a pronounced mismatch between data availability and control requirements. To address this challenge, this thesis investigates the use of physics-informed neural networks to forecast photovoltaic (PV) power generation in spatially distributed PV systems at temporal resolutions on the order of seconds under temporal data sparsity.
The proposed PINN incorporates partial physical knowledge of atmospheric processes through a spatiotemporal cloud motion equation, enabling physically plausible generalization to unseen time steps and conditions. To generate multi-step forecasts at temporal resolutions on the order of seconds, a recursive forecasting framework is developed. In addition, an improved training strategy is proposed in which the model is trained using its own recursively generated forecasts, thereby reducing the mismatch between training and inference. Finally, a novel correlation-based collocation point sampling strategy is developed to generate physically plausible and statistically representative collocation points, thereby supporting effective physics-based regularization.
The proposed methods are evaluated through two complementary case studies. In a case study based on a selected region in France, the PINN with the improved training strategy demonstrates a clear advantage under challenging atmospheric conditions compared to a persistence model, which assumes constant behavior over the forecast horizon. It also achieves superior data efficiency relative to purely data-driven neural networks and improved computational efficiency over a physics-only model during inference. The proposed collocation point sampling strategy also consistently outperforms uniform sampling.
In addition, a real-world case study based on a measurement site in Hawaii validates the forecasting framework under a realistic PV deployment scenario in which available data are not only temporally sparse but also satellite-derived. Because such data provide only an approximate, temporally and spatially smoothed representation of the true atmospheric conditions affecting the PV system, this setting introduces an additional data-reality mismatch. Nevertheless, the framework remains capable of producing meaningful forecasts at temporal resolutions on the order of seconds, establishing its practical applicability for PV systems worldwide without local measurement infrastructure.
The proposed PINN incorporates partial physical knowledge of atmospheric processes through a spatiotemporal cloud motion equation, enabling physically plausible generalization to unseen time steps and conditions. To generate multi-step forecasts at temporal resolutions on the order of seconds, a recursive forecasting framework is developed. In addition, an improved training strategy is proposed in which the model is trained using its own recursively generated forecasts, thereby reducing the mismatch between training and inference. Finally, a novel correlation-based collocation point sampling strategy is developed to generate physically plausible and statistically representative collocation points, thereby supporting effective physics-based regularization.
The proposed methods are evaluated through two complementary case studies. In a case study based on a selected region in France, the PINN with the improved training strategy demonstrates a clear advantage under challenging atmospheric conditions compared to a persistence model, which assumes constant behavior over the forecast horizon. It also achieves superior data efficiency relative to purely data-driven neural networks and improved computational efficiency over a physics-only model during inference. The proposed collocation point sampling strategy also consistently outperforms uniform sampling.
In addition, a real-world case study based on a measurement site in Hawaii validates the forecasting framework under a realistic PV deployment scenario in which available data are not only temporally sparse but also satellite-derived. Because such data provide only an approximate, temporally and spatially smoothed representation of the true atmospheric conditions affecting the PV system, this setting introduces an additional data-reality mismatch. Nevertheless, the framework remains capable of producing meaningful forecasts at temporal resolutions on the order of seconds, establishing its practical applicability for PV systems worldwide without local measurement infrastructure.
The increasing integration of renewable energy sources into power systems, characterized by their variability and inherent lack of inertia, presents significant challenges for the load frequency control problem, as large frequency fluctuations can cause equipment damage or even blackouts. Additionally, the large geographical size and complexity of today’s power systems require a multi-agent control strategy that is scalable and computationally efficient for real-time control.
This thesis proposes two novel control structures that integrate decentralized Model Predictive Control (MPC) with residual reinforcement learning based on the Deep Deterministic Policy Gradient (DDPG) algorithm. In the first structure, each area is controlled by a decentralized MPC, and a centralized coordinating residual DDPG layer is added on top. In the second structure, the decentralized MPC layer is combined with a distributed coordinating residual DDPG layer. The second structure is more scalable, but limits every DDPG controller to partial system observability. To effectively test and evaluate the novel control strategies, the European Economic Area Electricity Network Benchmark (EEA-ENB) is used.
Both structures share four key ideas: 1) Due to the coupling of areas in the EEA-ENB, the resulting power system is unstable, making it difficult to train the DDPG agent. To overcome this, the decentralized MPC layer enforces baseline stability, enabling the DDPG agent to learn a residual input to improve coordination between areas. 2) The coordinating DDPG layer is trained offline, shifting the computational burden away from online control. 3) By providing a meaningful baseline, decentralized MPC removes the need for the DDPG agent to learn entirely from scratch, which increases the sample efficiency. 4) The baseline allows the DDPG agent to learn in a smaller action space, which reduces exploration difficulties and improves the accuracy.
The proposed structures are compared against the centralized MPC, distributed MPC based on the alternating direction method of multipliers, and decentralized MPC in a four and six-area case study. Simulation results demonstrate that the coordinating residual input from the centralized or distributed DDPG layer significantly reduces the performance gap between decentralized MPC and the optimal centralized MPC solution. The centralized DDPG layer reduces the gap by 69.0% in the four-area case and 76.3% in the six-area case, while the distributed variant achieves reductions of 49.5% and 87.3%, respectively. Although the performance of the developed control structures does not fully match that of distributed MPC, the computational cost is at least 15 times lower. The distributed DDPG variant requires longer offline training time compared to the centralized DDPG method, but improves the scalability. To fully validate their performance and scalability, future work should implement the control structures on the entire EEA-ENB network. ...
This thesis proposes two novel control structures that integrate decentralized Model Predictive Control (MPC) with residual reinforcement learning based on the Deep Deterministic Policy Gradient (DDPG) algorithm. In the first structure, each area is controlled by a decentralized MPC, and a centralized coordinating residual DDPG layer is added on top. In the second structure, the decentralized MPC layer is combined with a distributed coordinating residual DDPG layer. The second structure is more scalable, but limits every DDPG controller to partial system observability. To effectively test and evaluate the novel control strategies, the European Economic Area Electricity Network Benchmark (EEA-ENB) is used.
Both structures share four key ideas: 1) Due to the coupling of areas in the EEA-ENB, the resulting power system is unstable, making it difficult to train the DDPG agent. To overcome this, the decentralized MPC layer enforces baseline stability, enabling the DDPG agent to learn a residual input to improve coordination between areas. 2) The coordinating DDPG layer is trained offline, shifting the computational burden away from online control. 3) By providing a meaningful baseline, decentralized MPC removes the need for the DDPG agent to learn entirely from scratch, which increases the sample efficiency. 4) The baseline allows the DDPG agent to learn in a smaller action space, which reduces exploration difficulties and improves the accuracy.
The proposed structures are compared against the centralized MPC, distributed MPC based on the alternating direction method of multipliers, and decentralized MPC in a four and six-area case study. Simulation results demonstrate that the coordinating residual input from the centralized or distributed DDPG layer significantly reduces the performance gap between decentralized MPC and the optimal centralized MPC solution. The centralized DDPG layer reduces the gap by 69.0% in the four-area case and 76.3% in the six-area case, while the distributed variant achieves reductions of 49.5% and 87.3%, respectively. Although the performance of the developed control structures does not fully match that of distributed MPC, the computational cost is at least 15 times lower. The distributed DDPG variant requires longer offline training time compared to the centralized DDPG method, but improves the scalability. To fully validate their performance and scalability, future work should implement the control structures on the entire EEA-ENB network. ...
The increasing integration of renewable energy sources into power systems, characterized by their variability and inherent lack of inertia, presents significant challenges for the load frequency control problem, as large frequency fluctuations can cause equipment damage or even blackouts. Additionally, the large geographical size and complexity of today’s power systems require a multi-agent control strategy that is scalable and computationally efficient for real-time control.
This thesis proposes two novel control structures that integrate decentralized Model Predictive Control (MPC) with residual reinforcement learning based on the Deep Deterministic Policy Gradient (DDPG) algorithm. In the first structure, each area is controlled by a decentralized MPC, and a centralized coordinating residual DDPG layer is added on top. In the second structure, the decentralized MPC layer is combined with a distributed coordinating residual DDPG layer. The second structure is more scalable, but limits every DDPG controller to partial system observability. To effectively test and evaluate the novel control strategies, the European Economic Area Electricity Network Benchmark (EEA-ENB) is used.
Both structures share four key ideas: 1) Due to the coupling of areas in the EEA-ENB, the resulting power system is unstable, making it difficult to train the DDPG agent. To overcome this, the decentralized MPC layer enforces baseline stability, enabling the DDPG agent to learn a residual input to improve coordination between areas. 2) The coordinating DDPG layer is trained offline, shifting the computational burden away from online control. 3) By providing a meaningful baseline, decentralized MPC removes the need for the DDPG agent to learn entirely from scratch, which increases the sample efficiency. 4) The baseline allows the DDPG agent to learn in a smaller action space, which reduces exploration difficulties and improves the accuracy.
The proposed structures are compared against the centralized MPC, distributed MPC based on the alternating direction method of multipliers, and decentralized MPC in a four and six-area case study. Simulation results demonstrate that the coordinating residual input from the centralized or distributed DDPG layer significantly reduces the performance gap between decentralized MPC and the optimal centralized MPC solution. The centralized DDPG layer reduces the gap by 69.0% in the four-area case and 76.3% in the six-area case, while the distributed variant achieves reductions of 49.5% and 87.3%, respectively. Although the performance of the developed control structures does not fully match that of distributed MPC, the computational cost is at least 15 times lower. The distributed DDPG variant requires longer offline training time compared to the centralized DDPG method, but improves the scalability. To fully validate their performance and scalability, future work should implement the control structures on the entire EEA-ENB network.
This thesis proposes two novel control structures that integrate decentralized Model Predictive Control (MPC) with residual reinforcement learning based on the Deep Deterministic Policy Gradient (DDPG) algorithm. In the first structure, each area is controlled by a decentralized MPC, and a centralized coordinating residual DDPG layer is added on top. In the second structure, the decentralized MPC layer is combined with a distributed coordinating residual DDPG layer. The second structure is more scalable, but limits every DDPG controller to partial system observability. To effectively test and evaluate the novel control strategies, the European Economic Area Electricity Network Benchmark (EEA-ENB) is used.
Both structures share four key ideas: 1) Due to the coupling of areas in the EEA-ENB, the resulting power system is unstable, making it difficult to train the DDPG agent. To overcome this, the decentralized MPC layer enforces baseline stability, enabling the DDPG agent to learn a residual input to improve coordination between areas. 2) The coordinating DDPG layer is trained offline, shifting the computational burden away from online control. 3) By providing a meaningful baseline, decentralized MPC removes the need for the DDPG agent to learn entirely from scratch, which increases the sample efficiency. 4) The baseline allows the DDPG agent to learn in a smaller action space, which reduces exploration difficulties and improves the accuracy.
The proposed structures are compared against the centralized MPC, distributed MPC based on the alternating direction method of multipliers, and decentralized MPC in a four and six-area case study. Simulation results demonstrate that the coordinating residual input from the centralized or distributed DDPG layer significantly reduces the performance gap between decentralized MPC and the optimal centralized MPC solution. The centralized DDPG layer reduces the gap by 69.0% in the four-area case and 76.3% in the six-area case, while the distributed variant achieves reductions of 49.5% and 87.3%, respectively. Although the performance of the developed control structures does not fully match that of distributed MPC, the computational cost is at least 15 times lower. The distributed DDPG variant requires longer offline training time compared to the centralized DDPG method, but improves the scalability. To fully validate their performance and scalability, future work should implement the control structures on the entire EEA-ENB network.
Driven by the rapid integration of Renewable Energy Sources (RESs) and the growing elec- trification of transport, heating, and industry, the Dutch power grid is being fundamentally reshaped. While essential for meeting climate goals, these developments introduce significant operational challenges, including higher uncertainty in power production and congestion risks. Existing approaches for Congestion Management (CM) often neglect the stochastic nature of RESs generation, rely on simplified network representations, or overlook real-world market constraints.
This thesis addresses these gaps by formulating the Dutch market-based CM problem as a Chance-Constrained Model Predictive Control (CC-MPC) framework. A linearized model of the Dutch high-voltage network is employed within a CC-MPC scheme that incorporates flexibility offers through integer decision variables. Uncertainty in RESs generation is cap- tured using an Seasonal AutoRegressive Integrated Moving-Average (SARIMA) model for each production region in the network, enabling a probabilistic treatment of forecast errors. To mitigate conservatism in the chance constraints, a Reinforcement Learning (RL) approach is introduced to adaptively tune the uncertainty model. The resulting stochastic disturbance trajectories are used in a sampling-based approximation of the CC-MPC, optimising conges- tion mitigation decisions under uncertainty.
The proposed methodology is validated using real-world data from the Dutch energy data ex- change platform Energie Data Services Nederland (EDSN), including operational data from Grid Operators Platform for AnCillary Services (GOPACS), the national CM platform. Re- sults demonstrate that the RL-enhanced CC-MPC achieves improved constraint satisfaction compared to other methods. Overall, this work contributes to the current literature by devel- oping a rigorous framework for market-based CM under uncertainty, aimed at ensuring the reliable and cost-effective operation of future renewable-dominated power systems. ...
This thesis addresses these gaps by formulating the Dutch market-based CM problem as a Chance-Constrained Model Predictive Control (CC-MPC) framework. A linearized model of the Dutch high-voltage network is employed within a CC-MPC scheme that incorporates flexibility offers through integer decision variables. Uncertainty in RESs generation is cap- tured using an Seasonal AutoRegressive Integrated Moving-Average (SARIMA) model for each production region in the network, enabling a probabilistic treatment of forecast errors. To mitigate conservatism in the chance constraints, a Reinforcement Learning (RL) approach is introduced to adaptively tune the uncertainty model. The resulting stochastic disturbance trajectories are used in a sampling-based approximation of the CC-MPC, optimising conges- tion mitigation decisions under uncertainty.
The proposed methodology is validated using real-world data from the Dutch energy data ex- change platform Energie Data Services Nederland (EDSN), including operational data from Grid Operators Platform for AnCillary Services (GOPACS), the national CM platform. Re- sults demonstrate that the RL-enhanced CC-MPC achieves improved constraint satisfaction compared to other methods. Overall, this work contributes to the current literature by devel- oping a rigorous framework for market-based CM under uncertainty, aimed at ensuring the reliable and cost-effective operation of future renewable-dominated power systems. ...
Driven by the rapid integration of Renewable Energy Sources (RESs) and the growing elec- trification of transport, heating, and industry, the Dutch power grid is being fundamentally reshaped. While essential for meeting climate goals, these developments introduce significant operational challenges, including higher uncertainty in power production and congestion risks. Existing approaches for Congestion Management (CM) often neglect the stochastic nature of RESs generation, rely on simplified network representations, or overlook real-world market constraints.
This thesis addresses these gaps by formulating the Dutch market-based CM problem as a Chance-Constrained Model Predictive Control (CC-MPC) framework. A linearized model of the Dutch high-voltage network is employed within a CC-MPC scheme that incorporates flexibility offers through integer decision variables. Uncertainty in RESs generation is cap- tured using an Seasonal AutoRegressive Integrated Moving-Average (SARIMA) model for each production region in the network, enabling a probabilistic treatment of forecast errors. To mitigate conservatism in the chance constraints, a Reinforcement Learning (RL) approach is introduced to adaptively tune the uncertainty model. The resulting stochastic disturbance trajectories are used in a sampling-based approximation of the CC-MPC, optimising conges- tion mitigation decisions under uncertainty.
The proposed methodology is validated using real-world data from the Dutch energy data ex- change platform Energie Data Services Nederland (EDSN), including operational data from Grid Operators Platform for AnCillary Services (GOPACS), the national CM platform. Re- sults demonstrate that the RL-enhanced CC-MPC achieves improved constraint satisfaction compared to other methods. Overall, this work contributes to the current literature by devel- oping a rigorous framework for market-based CM under uncertainty, aimed at ensuring the reliable and cost-effective operation of future renewable-dominated power systems.
This thesis addresses these gaps by formulating the Dutch market-based CM problem as a Chance-Constrained Model Predictive Control (CC-MPC) framework. A linearized model of the Dutch high-voltage network is employed within a CC-MPC scheme that incorporates flexibility offers through integer decision variables. Uncertainty in RESs generation is cap- tured using an Seasonal AutoRegressive Integrated Moving-Average (SARIMA) model for each production region in the network, enabling a probabilistic treatment of forecast errors. To mitigate conservatism in the chance constraints, a Reinforcement Learning (RL) approach is introduced to adaptively tune the uncertainty model. The resulting stochastic disturbance trajectories are used in a sampling-based approximation of the CC-MPC, optimising conges- tion mitigation decisions under uncertainty.
The proposed methodology is validated using real-world data from the Dutch energy data ex- change platform Energie Data Services Nederland (EDSN), including operational data from Grid Operators Platform for AnCillary Services (GOPACS), the national CM platform. Re- sults demonstrate that the RL-enhanced CC-MPC achieves improved constraint satisfaction compared to other methods. Overall, this work contributes to the current literature by devel- oping a rigorous framework for market-based CM under uncertainty, aimed at ensuring the reliable and cost-effective operation of future renewable-dominated power systems.