On the Comparison of Stochastic Model Predictive Control Strategies Applied to a Hydrogen-based Microgrid

In this paper, a performance comparison among three well-known stochastic model predictive control approaches, namely, multi-scenario, tree-based, and chance-constrained model predictive control is presented. To this end, three predictive controllers have been designed and implemented in a real renewable-hydrogen-based microgrid. The experimental set-up includes a PEM electrolyzer, lead-acid batteries, and a PEM fuel cell as main equipment. The real experimental results show significant differences from the plant components, mainly in terms of use of energy, for each implemented technique. Effectiveness, performance, advantages, and disadvantages of these techniques are extensively discussed and analyzed to give some valid criteria when selecting an appropriate stochastic predictive controller.Ministerio de Economía y Competitividad DPI2013-46912-C2-1-RMinisterio de Economía y Competitividad DPI2013-482443-C2-1-

On the comparison of stochastic model predictive control strategies applied to a hydrogen-based microgrid

© . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/In this paper, a performance comparison among three well-known stochastic model predictive control approaches, namely, multi-scenario, tree-based, and chance-constrained model predictive control is presented. To this end, three predictive controllers have been designed and implemented in a real renewable-hydrogen-based microgrid. The experimental set-up includes a PEM electrolyzer, lead-acid batteries, and a PEM fuel cell as main equipment. The real experimental results show significant differences from the plant components, mainly in terms of use of energy, for each implemented technique. Effectiveness, performance, advantages, and disadvantages of these techniques are extensively discussed and analyzed to give some valid criteria when selecting an appropriate stochastic predictive controller.Peer ReviewedPostprint (author's final draft

Wirelessly Enabled Control of Cyber-Physical Infrastructure with Applications to Hydronic Systems.

Civil infrastructure systems, such as transportation networks, pipe networks, electrical grids, and building environments, are typically managed and controlled with outdated, inefficient, and minimally automated legacy controllers. This is apparent from documented oil pipeline leaks, broad electrical outages, and power plant failures. The relatively recent advents of small inexpensive microcontrollers and low-power wireless networking technologies has revealed opportunities for better managing the operational effectiveness of civil infrastructure systems. Academic research in this field is maturing, yet the field remains in its nascent years of commercial viability, focusing mainly on low data-rate sensing with centralized processing. Little focus has been on distributed wireless control systems for civil infrastructure. This dissertation follows the development and utilization of a new cyber-physical system (CPS) architecture for civil infrastructure. Embedded computing power is distributed throughout the physical systems and global objectives are met with the aid of wireless information exchange. The Martlet wireless controller node was conceived during the first part of this thesis to enable this objective of wirelessly distributed CPS. Once produced, the Martlet was used to realize such a controller, motivated by an application in hydronic cooling systems. The design of the proposed controller began with a study concerning models and objective functions for the control of bilinear systems, like those found in hydronics, when constrained by the resources of a wireless control node. The results showed that previous work with linear quadratic controllers could be improved by using nonlinear models and explicit objective functions. An agent-based controller utilizing the proposed bilinear model-predictive control algorithm, was then developed accounting for the limitation of, and leveraging the advantages of, wireless control nodes in order to regulate a hydronic system with hybrid dynamics. The resulting Martlet based control system was compared to traditional benchmark controllers and shown to achieve adequate performance, with the added benefits of a wireless CPS. These developments in wirelessly distributed control of complex systems are presented not only with the tested hydronic systems in mind, but with the goal of extending this technology to improve the performance and reliability of a wide variety of controlled cyber-physical civil infrastructure systems.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/107310/1/mbkane_1.pd

Model based forecasting for demand response strategies

The incremental deployment of decentralized renewable energy sources in the distribution grid is triggering a paradigm change for the power sector. This shift from a centralized structure with big power plants to a decentralized scenario of distributed energy resources, such as solar and wind, calls for a more active management of the distribution grid. Conventional distribution grids were passive systems, in which the power was flowing unidirectionally from upstream to downstream. Nowadays, and increasingly in the future, the penetration of distributed generation (DG), with its stochastic nature and lack of controllability, represents a major challenge for the stability of the network, especially at the distribution level. In particular, the power flow reversals produced by DG cause voltage excursions, which must be compensated. This poses an obstacle to the energy transition towards a more sustainable energy mix, which can however be mitigated by using a more active approach towards the control of the distribution networks. Demand side management (DSM) offers a possible solution to the problem, allowing to actively control the balance between generation, consumption and storage, close to the point of generation. An active energy management implies not only the capability to react promptly in case of disturbances, but also to ability to anticipate future events and take control actions accordingly. This is usually achieved through model predictive control (MPC), which requires a prediction of the future disturbances acting on the system. This thesis treat challenges of distributed DSM, with a particular focus on the case of a high penetration of PV power plants. The first subject of the thesis is the evaluation of the performance of models for forecasting and control with low computational requirements, of distributed electrical batteries. The proposed methods are compared by means of closed loop deterministic and stochastic MPC performance. The second subject of the thesis is the development of model based forecasting for PV power plants, and methods to estimate these models without the use of dedicated sensors. The third subject of the thesis concerns strategies for increasing forecasting accuracy when dealing with multiple signals linked by hierarchical relations. Hierarchical forecasting methods are introduced and a distributed algorithm for reconciling base forecasters is presented. At the same time, a new methodology for generating aggregate consistent probabilistic forecasts is proposed. This method can be applied to distributed stochastic DSM, in the presence of high penetration of rooftop installed PV systems. In this case, the forecasts' errors become mutually dependent, raising difficulties in the control problem due to the nontrivial summation of dependent random variables. The benefits of considering dependent forecasting errors over considering them as independent and uncorrelated, are investigated. The last part of the thesis concerns models for distributed energy markets, relying on hierarchical aggregators. To be effective, DSM requires a considerable amount of flexible load and storage to be controllable. This generates the need to be able to pool and coordinate several units, in order to reach a critical mass. In a real case scenario, flexible units will have different owners, who will have different and possibly conflicting interests. In order to recruit as much flexibility as possible, it is therefore importan