In isolated power systems with low rotational inertia, fast-frequency control strategies are required to maintain frequency stability. Furthermore, with limited resources in such
isolated systems, the deployed control strategies have to provide the flexibility to handle operational constraints so the controller is optimal from a technical as well as an economical point-ofview. In this paper, a model predictive control (MPC) approach is
proposed to maintain the frequency stability of these low inertia power systems, such as microgrids. Given a predictive model of the system, MPC computes control actions by recursively solving a finite-horizon, online optimization problem that satisfies peak
power output and ramp-rate constraints. MATLAB/Simulink based simulations show the effectiveness of the controller to reduce frequency deviations and the rate-of-change-of-frequency (ROCOF) of the system. By proper selection of controller parameters,
desired performance can be achieved while respecting the physical constraints on inverter peak power and/or ramp-rates.
more »
« less
A Model Predictive Approach for Voltage Support in Microgrids using Energy Storage Systems
Low voltage microgrid systems are characterized by high sensitivity to both active and reactive power for voltage support. Also, the operational conditions of microgrids connected to active distribution systems are time-varying. Thus, the ideal controller to provide voltage support must be flexible enough to handle technical and operational constraints. This paper proposes a model predictive control (MPC) approach to provide dynamic voltage support using energy storage systems. This approach uses a simplified predictive model of the system along with operational constraints to solve an online finite-horizon optimization problem. Control signals are then computed such that the defined cost function is minimized. By proper selection of MPC weighting parameters, the quality of service provided can be adjusted to achieve the desired performance. A simulation study in Matlab/Simulink validates the proposed approach for a simplified version of a 100 kVA, 208 V microgrid using typical parameters. Results show that performance of the voltage support can be adjusted depending on the choice of weight and constraints of the controller.
more »
« less
- Award ID(s):
- 1949921
- PAR ID:
- 10345432
- Date Published:
- Journal Name:
- 2021 IEEE Power & Energy Society General Meeting (PESGM)
- Page Range / eLocation ID:
- 1 to 5
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Enhancing grid resilience is proposed through the integration of distributed energy resources (DERs) with microgrids. Due to the diverse nature of DERs, there is a need to explore the optimal combined operation of these energy sources within the framework of microgrids. As such, this paper presents the design, implementation and validation of a Model Predictive Control (MPC)-based secondary control scheme to tackle two challenges: optimal islanded operation, and optimal re-synchronization of a microgrid. The MPC optimization algorithm dynamically adjusts input signals, termed manipulated variables, for each DER within the microgrid, including a gas turbine, an aggregate photovoltaic (PV) unit, and an electrical battery energy storage (BESS) unit. To attain optimal islanded operation, the secondary-level controller based on Model Predictive Control (MPC) was configured to uphold microgrid functionality promptly following the islanding event. Subsequently, it assumed the task of power balancing within the microgrid and ensuring the reliability of the overall system. For optimal re-synchronization, the MPC-based controller was set to adjust the manipulated variables to synchronize voltage and angle with the point of common coupling of the system. All stages within the microgrid operation were optimally achieved through one MPC-driven control system, where the controller can effectively guide the system to different goals by updating the MPC’s target reference. More importantly, the results show that the MPC-based control scheme is capable of controlling different DERs simultaneously, mitigating potentially harmful transient rotor torques from the re-synchronization as well as maintaining the microgrid within system performance requirements.more » « less
-
null (Ed.)Hybrid-electric aircraft represent an important step in the transition from conventional fuel-based propulsion to fully-electric aircraft. For hybrid power systems, overall aircraft performance and efficiency highly depend on the coordination of the fuel and electrical systems and the ability to effectively control state and input trajectories at the limits of safe operation. In such a safety-critical application, the chosen control strategy must ensure the closed-loop system adheres to these operational limits. While hierarchical Model Predictive Control (MPC) has proven to be a computationally efficient approach to coordinated control of complex systems across multiple timescales, most formulations are not supported by theoretical guarantees of actuator and state constraint satisfaction. To provide guaranteed constraint satisfaction, this paper presents set-based hierarchical MPC of a 16 state hybrid-electric aircraft power system. Within the proposed two-level vertical hierarchy, the long-term control decisions of the upper-level controller and the short-term control decisions of the lower-level controller are coordinated through the use of waysets. Simulation results show the benefits of this coordination in the context of hybrid-electric aircraft performance and demonstrate the practicality of applying set-based hierarchical MPC to complex multi-timescale systems.more » « less
-
We propose an approach to design a Model Predictive Controller (MPC) for constrained Linear Time Invariant systems performing an iterative task. The system is subject to an additive disturbance, and the goal is to learn to satisfy state and input constraints robustly. Using disturbance measurements after each iteration, we construct Confidence Support sets, which contain the true support of the disturbance distribution with a given probability. As more data is collected, the Confidence Supports converge to the true support of the disturbance. This enables design of an MPC controller that avoids conservative estimate of the disturbance support, while simultaneously bounding the probability of constraint violation. The efficacy of the proposed approach is then demonstrated with a detailed numerical example.more » « less
-
The active control of stormwater systems is a potential solution to increased street flooding in low-lying, low-relief coastal cities due to climate change and accompanying sea level rise. Model predictive control (MPC) has been shown to be a successful control strategy generally and as well as for managing urban drainage specifically. This research describes and demonstrates the implementation of MPC for urban drainage systems using open source software (Python and The United States Environmental Protection Agency (EPA) Storm Water Management Model (SWMM5). The system was demonstrated using a simplified use case in which an actively-controlled outlet of a detention pond is simulated. The control of the pond’s outlet influences the flood risk of a downstream node. For each step in the SWMM5 model, a series of policies for controlling the outlet are evaluated. The best policy is then selected using an evolutionary algorithm. The policies are evaluated against an objective function that penalizes primarily flooding and secondarily deviation of the detention pond level from a target level. Freely available Python libraries provide the key functionality for the MPC workflow: step-by-step running of the SWMM5 simulation, evolutionary algorithm implementation, and leveraging parallel computing. For perspective, the MPC results were compared to results from a rule-based approach and a scenario with no active control. The MPC approach produced a control policy that largely eliminated flooding (unlike the scenario with no active control) and maintained the detention pond’s water level closer to a target level (unlike the rule-based approach).more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/PESGM46819.2021.9638037
