Battery Sizing for PV Power Plants Under Regulations Using Randomized Algorithms
The increasing amount of PV (photovoltaic) power plants comes along with an increased instability in the power grid due to the high uncertainty of the PV power production. As a stabilizing measure, grid operators introduce regulations on the injected power profiles comprising the obligation to declare in advance the predicted power production as well as penal- ties which apply in case these previously declared production profiles were not respected. In order to meet these regulations power plant owners are forced to invest into expensive storage capacities. In this work an algorithm is proposed which allows to determine the optimal battery size that maximizes the to-be- expected revenue of such an installation for a given regulative framework. Moreover the scheme explicitly takes into account the uncertainty in the PV power production and it provides guaranteed lower bounds on the to-be-expected revenue at a configurable probability. The underlying method allowing to achieve these objectives are randomized algorithms
Reference
P. Pflaum, M. Alamir, M. Y. Lamoudi. Battery Sizing for PV power plants under regulations using randomized algorithms. Renewable Energy (Elsevier), (2017).
Robust energy management strategy for electric vehicle charging stations
Electric vehicle charging stations (EVCS) come along with great challenges for the power grid due to their highly uncertain load characteristic. This is particularly the case for charging stations located in non-residential areas such as commercial centers, company sites or car-rental stations. For a safe and sustainable operation of the power grid, distribution system operators (DSO) require reliable load forecasts of such charging stations. In this paper a robust EVCS management strategy is proposed which provides a day-ahead upper limit profile of the EVCS’s power consumption. In real-time this upper limit profile is strictly respected while guaranteeing – at a configurable probability – the QoS (Quality of Service). The strategy is based on randomized algorithms and relies on a statistic occupancy model of the EVCS while not requiring any online forecasts of each EVs’ arrival- and departure schedules. In a case-study based on statistic data which has been provided by the Euref Campus in Berlin, the feasibility and relevance of the proposed approach is demonstrated.
Reference
P. Pflaum, M. Alamir, M. Y. Lamoudi. Robust energy management strategy for electric vehicle charging stations. IEEE Transactions on Control System Techniology (2017).
Scalability study for a hierarchical NMPC scheme for resource sharing problems
This paper deals with the computational efficiency evaluation of a hierarchical DMPC (distributed model predictive control) framework for resource sharing problems. The provided DMPC framework is based on a dual decomposition of the centralized open-loop controller which is decomposed into several subproblems and one coordinator problem. At coordinator level the bundle method is used in order to recover the globally optimal solution through an iterative process.
The main focus of this paper is a detailed discussion of the impact of the bundle method’s parametrization on the computational performance of the whole scheme. Additionally a qualitative comparison with a similar scheme based on primal decomposition is provided and some rules of thumb for deter- mining an effective parametrization of the bundle method are established. In the provided simulations the scheme is applied to a large-scale problem of the smart district context. More precisely the centralized optimization problem of a district composed of 1000 buildings sharing a globally limited power resource is able be solved to optimality using our proposed framework in around 3 seconds.
Reference
P. Pflaum, M. Alamir, M. Y. Lamoudi. Scalability study for a hierarchical NMPC scheme for resource sharing problems. Proceedings of the European Control Conference, ECC2015, Linz, Austria, 2015. [download]
Distributed constrained model predictive control for energy management in buildings. Part 1: Zone Model Predictive Control
In this paper, a distributed model predictive control to manage the actuation of the whole actuators (heating/cooling, ventilation, lighting, shading) in a multi-zone building to control comfort parameters (temperature, indoor CO2 level and indoor illuminance). The control process is performed in a distributed fashion and handle variable prices as well as resourcu limitation in a context of a multi energy source building. To this end, we firstly present a zonal non linear model predictive controller which is concerned by zonal decision making, we then provide a coordination scheme based on a primal decomposition to adresse the resource allocation problem that happens in the presence of global constraints on power consumption. We finally provide some simulation results attesting the fast convergence of control algorithm and the benefit of the controller.
Reference
M. Y. Lamoudi, M. Alamir and P. Béguery. Model predictive control for energy management in buildings. Part 1: Zone Model Predictive Control. IFAC workshop on Nonlinear Model Predictive Control, NMPC2012, The Netherlands. August, 2012. [download]
Model predictive control for energy management in buildings. Part 2: Distributed Model Predictive Control
In this second part of the paper dedicated to energy management in buildings a Distributed Model Predictive Control strategy is proposed in order to tackle the control problem of a large building submitted to global power limitations and disposing of a storage device (electrical battery). The proposed scheme is based on previously designed Model Predictive Controllers responsible of managing the comfort quantities at the zone level. The proposed framework addresses the case of power specific limitations and dynamically varying prices. Numerical simulations are proposed for a realistic buildings model including 20 zones in order to assess the efficiency and the real time implementability of the proposed framework.
Reference
M. Y. Lamoudi, M. Alamir and P. Béguery. Model predictive control for energy management in buildings. Part 2: Distributed Model Predictive Control. IFAC workshop on Nonlinear Model Predictive Control, NMPC2012, The Netherlands. August, 2012. [download]
Distributed constrained Model Predictive Control based on bundle method for building energy management
This paper presents a distributed Model Predictive Control framework based on a primal decomposition and a bundle method to control the indoor environmental conditions in a multisource/multizone building. The control aims to min- imize the total energy cost under restrictions on global power consumption and local constraints on comfort and saturations on actuators. Moreover, each power source is supposed to have a time varying tarification. The distributed Model Predictive Control algorithm is based on two layers: a zone layer which is responsible of local zone decisions and a coordination layer that handles decisions that go beyond the scope of the zone. Simulation results are finally provided for a threu zones building with a local power production and changing rate grid power. A computational study is also provided, this to attest the effectiveness and the real-time implementability of the proposed control method.
Reference
M. Y. Lamoudi, M. Alamir and P. Béguery. Distributed constrained Model Predictive Control based on bundle method for building energy management. Proceedings of the 50th IEEE Conference on Decision and Control and European Control Conference, Orlando, FL. USA, 12-15 December, 2011. [download]
Unified NMPC for Multi-Variable Control in Smart Buildings
In this paper, the problem of minimizing energy consumption of a building zone of under pre-assigned multi-variable comfort conditions and changing rates is addressed. The solution involves the use of a parameterized multi-variable Nonlinear Model Predictive Control (NMPC) that manages the actuation of heating/cooling, ventilation, lighting and blinds devyces. Simulations of the resulting closed-loop in winter and summer seasons under varying rate profile are proposed to assess its efficiency. Moreover a sensitivity analysis is conducted to show how the comfort level assignment impacts the level of energy consumption
Reference
M. Y. Lamoudi, M. Alamir and P. Béguery. Unified NMPC for Multi-Variable Control in Smart Buildings. Proceedings of the IFAC World Congress, MIlano, Italy, (2011). [download]
A distributed-in-time NMPC-based coordination mechanism for resource sharing problems
In this chapter, a hierarchical model predictive control framework is pre- sented for a network of subsystems that are submitted to general resource sharing constraints. The method is based on a primal decomposition of the centralized open- loop optimization problem over several subsystems. A coordinator is responsible of adjusting the parameters of the problems that are to be solved by each subsystem. A distributed-in-time feature is combined with a bundle method at the coordination layer that enables to enhance the performance and the real-time implementability of the proposed approach. The scheme performance is assessed using a real-life energy coordination problem in a building involving 20 zones that have to share a limited amount of total power.
Reference
M. Y. Lamoudi, M. Alamir and P. Béguery. A Distributed-in-time NMPC-Based Mechanism For Resource Sharing Problems. In Distributed MPC Made Easy. J. Maestre and R. R. Negenborn (Eds). Springer Verlag. (2012). [download]
Multisource elevator energy optimization and control
Nowadays,devices to ensure autonomy in case of grid failure. This paper presents a method that takes advantage of these storage devices to optimize energy consumption and cost. The optimization is achieved by two controllers: a high-level one (using linear pro- gramming) and a low-level one (using simple rules). Preliminary results indicate that this method is 35% better than a naive rule-based approach in our context.
Reference
Ch. Desdouits, M. Alamir, V. Boutin, C. Le Pape. Multisource elevator energy optimization and control. Proceedings of the European Control Conference, ECC2015, Linz, Austria, 2015. [download]
Scalability Study for a hierarchical NMPC Scheme for resource sharing problems
This paper deals with the computational efficiency evaluation of a hierarchical DMPC (distributed model predictive control) framework for resource sharing problems. The provided DMPC framework is based on a dual decomposition of the centralized open-loop controller which is decomposed into several subproblems and one coordinator problem. At coordinator level the bundle method is used in order to recover the globally optimal solution through an iterative process.
The main focus of txis paper is a detailed discussion of the impact of the bundle method’s parametrization on the computational performance of the whole scheme. Additionally a qualitative comparison with a similar scheme based on primal decomposition is provided and some rules of thumb for deter- mining an effective parametrization of the bundle method are established. In the provided simulations the scheme is applied to a large-scale problem of the smart district context. More precisely the centralized optimization problem of a district composed of 1000 buildings sharing a globally limited power resource is able be solved to optimality using our proposed framework in around 3 seconds.
Reference
Scalability study for a hierarchical NMPC scheme for resource sharing problems. P. Pflaum, M. Alamir and M. Y. Lamoudi. Proceedings of the European Control Conference, ECC2015, Linz, Austria, 2015. [download]
The Sourcing Problem: Energy Optimization of a MultiSource Elevator.
As the interest for rewulating energy usage and for the demand-response market is growing, new energy management algorithms emerge. In this paper, we propose a formalization of the sourcing problem and its application to a multisource elevator. We propose a linear formulation that, coupled to a low level rule-based controller, can solve this problem. We show in the experiments that a trade-off has to be done between reducing consumption peaks and minimizing the energy bill.
Reference
Ch. Dedouits, M. Alamir, R. Giroudeau and C. Le Pape. The Sourcing Problem : Energy Optimization of a MultiSource Elevator 13th International Conference on Informatics in Control ICINCO. Lisbon, Portugal, 2016.
Other publication on Smart Buildings and Districts
P. Pflaum, M. Alamir and M. Y. Lamoudi. Comparison of a primal and a dual decomposition for distributed MPC in smart districts. Proceeding of the IEEE Symposium SmartGridComm, Venice, Italy, 2014.
