Fast and Reliable Primary Frequency Reserves From Refrigerators with Decentralized Stochastic Control

Due to increasing shares of renewable energy sources, more frequency reserves are required to maintain power system stability. In this paper, we present a decentralized control scheme that allows a large aggregation of refrigerators to provide Primary Frequency Control (PFC) reserves to the grid based on local frequency measurements and without communication. The control is based on stochastic switching of refrigerators depending on the frequency deviation. We develop methods to account for typical lockout constraints of compressors and increased power consumption during the startup phase. In addition, we propose a procedure to dynamically reset the thermostat temperature limits in order to provide reliable PFC reserves, as well as a corrective temperature feedback loop to build robustness to biased frequency deviations. Furthermore, we introduce an additional randomization layer in the controller to account for thermostat resolution limitations, and finally, we modify the control design to account for refrigerator door openings. Extensive simulations with actual frequency signal data and with different aggregation sizes, load characteristics, and control parameters, demonstrate that the proposed controller outperforms a relevant state-of-the-art controller.Comment: 44 pages, 17 figures, 9 Tables, submitted to IEEE Transactions on Power System

Control of frequency in future power systems

Future power systems will face a significant challenge due to the reduced stability of frequency. The reduction of inertia drives this challenge due to the increasing level of power electronics connected to renewable energy sources. In this thesis, new control techniques,such as a new secondary frequency control, a control of a population of water heaters(WHs), and a control of a population of battery energy storage systems (BESSs), are studied. A fuzzy logic-based secondary frequency controller was developed to supplement the conventional frequency control in large synchronous generators. This controller is suitable for the provision of mandatory frequency response in the Great Britain (GB) power system, where an additional 10% power output for primary response and 10% for secondary response are required within ten seconds and thirty seconds respectively. The controller was demonstrated using a simplified GB power system and a multi-machine benchmark power system. The results showed that, following a disturbance, the controller improved frequency deviation and error compared to the conventional PI controller. Thus, the controller provides a stable frequency control in future power systems. A hierarchical control of a population of WHs and BESSs was used to provide frequency response services. This was based on two decision layers. The aggregator layer receives the states of WHs/BESSs and sends a command signal to each WH/BESS control layer. The hierarchical control enables the aggregator to choose the number of controllable WHs/BESSs and set the desired amount of responses to offer different frequency response services. As a result, it reduces the uncertainty associated with the response of the population during a frequency event. The WH/BESS controller provides a response based on the last command signal from the aggregator, the value of frequency deviation (ΔF) and the level of the water temperature or BESS state of charge (SoC). The WH/BESS controller provides a response even when a failure occurs in the communication with the aggregator control layer. The WH/BESS controller handles both negative and positive ΔF. Hence, the aggregated loads participate in both low and high frequency responses. The response of the population of BESSs goes from the highest to lowest SoC when the frequency falls and from the lowest to highest SoC when it rises. The response from WHs is from highest to lowest water temperature when the frequency drops. Thus, this reduces the risk of a simultaneous power change in a large number of controllable loads at the same time, which, in turn, reduces the impact. The dynamic behaviour of a population of WHs/BESSs was modelled based on the Markov chain to allow the aggregator to offer different frequency response services. A Markov-based model was also used to evaluate the effective capacity of aggregated WHs/BESSs during the frequency event. The Markov-based model was demonstrated on a simplified GB power system and the South-East Australian power system, considering different aggregation case studies