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Abstract
In this thesis, we consider control strategies for flexible distributed energy resources in the future intelligent energy system – the Smart Grid. The energy system is a largescale complex network with many actors and objectives in different hierarchical layers. Specifically the power system must supply electricity reliably to both residential and industrial consumers around the clock. More and more fluctuating renewable energy sources, like wind and solar, are integrated in the power system. Consequently, uncertainty in production starts to affect an otherwise controllable power production significantly. A Smart Grid calls for flexible consumers that can adjust their consumption based on the amount of green energy in the grid. This requires coordination through new largescale control and optimization algorithms. Trading of flexibility is key to drive power consumption in a sustainable direction. In Denmark, we expect that distributed energy resources such as heat pumps, and batteries in electric vehicles will mobilize part of the needed flexibility.
Our primary objectives in the thesis were threefold:
1.Simulate the components in the power system based on simple models from literature (e.g. heat pumps, heat tanks, electrical vehicle battery charging/discharging, wind farms, power plants).
2.Embed forecasting methodologies for the weather (e.g. temperature, solar radiation), the electricity consumption, and the electricity price in a predictive control system.
3.Develop optimization algorithms for largescale dynamic systems. This includes decentralized optimization and simulation on realistic largescale dynamic systems.
Chapter 1 introduces the power system, the markets, and the main actors. The objectives and control hierarchy is outlined while Aggregators are introduced as new actors.
Chapter 2 provides linear dynamical models of Smart Grid units: Electric Vehicles, buildings with heat pumps, refrigeration systems, solar collectors, heat storage tanks, power plants, and wind farms. The models can be realized as discrete time state space models that fit into a predictive control system.
Chapter 3 introduces Model Predictive Control (MPC) including state estimation, filtering and prediction for linear models.
Chapter 4 simulates the models from Chapter 2 with the certainty equivalent MPC from Chapter 3. An economic MPC minimizes the costs of consumption based on real electricity prices that determined the flexibility of the units. A predictive control system easily handles constraints, e.g. limitations in power consumption, and predicts the future behavior of a unit by integrating predictions of electricity prices, consumption, and weather variables. The simulations demonstrate the expected load shifting capabilities of the units that adapts to the given price predictions. We furthermore evaluated control performance in terms of economic savings for different control strategies and forecasts.
Chapter 5 describes and compares the proposed largescale Aggregator control strategies. Aggregators are assumed to play an important role in the future Smart Grid and coordinate a large portfolio of units. The developed economic MPC controllers interfaces each unit directly to an Aggregator. We developed several MPCbased aggregation strategies that coordinates the global behavior of a portfolio of units by solving a largescale optimization and control problem. We applied decomposition methods based on convex optimization, such as dual decomposition and operator splitting, and developed pricebased aggregator strategies.
Chapter 6 provides conclusions, contributions and future work.
The main scientific contributions can be summarized to:
•Linear dynamical models of flexible Smart Grid units: heat pumps in buildings, heat storage tanks, and electric vehicle batteries.
•Economic MPC that integrates forecasts in the control of these flexible units.
•Largescale distributed control strategies based on economic MPC, convex optimization, and decomposition methods.
•A Matlab toolbox including the modeled units for simulating a Smart Energy System with MPC.
Our primary objectives in the thesis were threefold:
1.Simulate the components in the power system based on simple models from literature (e.g. heat pumps, heat tanks, electrical vehicle battery charging/discharging, wind farms, power plants).
2.Embed forecasting methodologies for the weather (e.g. temperature, solar radiation), the electricity consumption, and the electricity price in a predictive control system.
3.Develop optimization algorithms for largescale dynamic systems. This includes decentralized optimization and simulation on realistic largescale dynamic systems.
Chapter 1 introduces the power system, the markets, and the main actors. The objectives and control hierarchy is outlined while Aggregators are introduced as new actors.
Chapter 2 provides linear dynamical models of Smart Grid units: Electric Vehicles, buildings with heat pumps, refrigeration systems, solar collectors, heat storage tanks, power plants, and wind farms. The models can be realized as discrete time state space models that fit into a predictive control system.
Chapter 3 introduces Model Predictive Control (MPC) including state estimation, filtering and prediction for linear models.
Chapter 4 simulates the models from Chapter 2 with the certainty equivalent MPC from Chapter 3. An economic MPC minimizes the costs of consumption based on real electricity prices that determined the flexibility of the units. A predictive control system easily handles constraints, e.g. limitations in power consumption, and predicts the future behavior of a unit by integrating predictions of electricity prices, consumption, and weather variables. The simulations demonstrate the expected load shifting capabilities of the units that adapts to the given price predictions. We furthermore evaluated control performance in terms of economic savings for different control strategies and forecasts.
Chapter 5 describes and compares the proposed largescale Aggregator control strategies. Aggregators are assumed to play an important role in the future Smart Grid and coordinate a large portfolio of units. The developed economic MPC controllers interfaces each unit directly to an Aggregator. We developed several MPCbased aggregation strategies that coordinates the global behavior of a portfolio of units by solving a largescale optimization and control problem. We applied decomposition methods based on convex optimization, such as dual decomposition and operator splitting, and developed pricebased aggregator strategies.
Chapter 6 provides conclusions, contributions and future work.
The main scientific contributions can be summarized to:
•Linear dynamical models of flexible Smart Grid units: heat pumps in buildings, heat storage tanks, and electric vehicle batteries.
•Economic MPC that integrates forecasts in the control of these flexible units.
•Largescale distributed control strategies based on economic MPC, convex optimization, and decomposition methods.
•A Matlab toolbox including the modeled units for simulating a Smart Energy System with MPC.
Original language  English 

Place of Publication  Kgs. Lyngby 

Publisher  Technical University of Denmark 
Number of pages  182 
Publication status  Published  2014 
Series  DTU Compute PHD2014 

Number  327 
ISSN  09093192 
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 1 Finished

Model Predictive Control for Smart Energy Systems
Halvgaard, R. F., Jørgensen, J. B., Madsen, H., Bindner, H. W., Chmielewski, D. J., Jones, C. N. & Poulsen, N. K.
Technical University of Denmark
01/11/2010 → 25/04/2014
Project: PhD