Flywheel systems are fast-acting energy storage solutions that could be effectively utilized to facilitate seamless adoptions for high penetration levels of variable power generation resources. This paper describes a real-world case study for the deployment of a 2 MW flywheel energy storage system to smooth the output power of a remotely located wind farm connected to the electricity grid of Ontario in Canada. Both the flywheel energy storage facility and the wind power plant are monitored and operated by the Independent Electricity System Operator (IESO) in Ontario. Also, the paper introduces a novel and practical Energy Management Controller (EMC) that has been implemented in the field to achieve the desired renewable smoothing. The performance of the implemented EMC has been investigated via changing three main input parameters: i) storage to wind ratio, ii) smoothing time, and iii) storage duration. To that end, seven case studies are conducted with different EMC input parameters to analyze the impacts of these parameters on the smoothing performance of the flywheel energy storage system.

This paper presents a novel $\mathcal {H}_{2}$ filter design procedure to optimally split the Frequency Regulation (FR) signal between conventional and fast regulating Energy Storage System (ESS) assets, considering typical Communication Delays (CDs). The filter is then integrated into a previously validated FR model of the Ontario Power System (OPS) including Battery and Flywheel ESSs, which is used to analyze the impact of these ESSs, CDs, and limited regulation capacity in the FR process in a real system. The proposed methodology to split the FR signal is also compared with the existing FR process, with the results showing that the proposed $\mathcal {H}_{2}$ filter design and signal splitting strategy can improve the FR process performance significantly, in terms of reducing the Area Control Error (ACE) signal, and thus reduce the need for regulation capacity.

This paper presents a Frequency Regulation (FR) model of a large interconnected power system including Energy Storage Systems (ESSs) such as Battery Energy Storage Systems (BESSs) and Flywheel Energy Storage Systems (FESSs), considering all relevant stages in the frequency control process. Communication delays are considered in the transmission of the signals in the FR control loop and ESSs, and their State of Charge (SoC) management model is considered. The system, ESSs and SoC components are modelled in detail from a FR perspective. The model is validated using real system and ESSs data, based on a practical transient stability model of the North American Eastern Interconnection (NAEI), and the results show that the proposed model accurately represents the FR process of a large interconnected power network including ESS, and can be used for long-term FR studies. The impact of communication delays and SoC management of ESS facilities in the Area Control Error (ACE) is also studied and discussed, as well as the computational efficiency of the proposed FR model.

This paper presents a detailed methodology to develop Marginal Rate of Technical Substitution (MRTS) curves, which can be used to optimally determine the appropriate substitution of traditional regulation signals with fast regulation signals, considering different Energy Storage System (ESS) technologies and discharging times, scenarios, and seasons. The presented work is based on the Ontario Power System (OPS) managed by the Independent Electricity System Operator (IESO) of Ontario, Canada. Different comparisons of the MRTS curves are carried out and the criteria used to obtain 16 average optimized MRTS curves, four per season, are presented. Finally, the 16 MRTS curves obtained for the IESO and their parameters are presented, and the use of these curves is explained through an example.

The declining costs for various distributed energy resources such as solar and energy storage is driving an increase in the penetration level of these resources at the grid’s edge. The electricity market operator must account for these changes to effectively plan the system’s demand, supply, and adequacy for various scenarios. This paper proposes a simplified methodology to create a probabilistic model of demand and supply which can be used to model resource adequacy as a function of temperature. This adequacy model is then translated to describe adequacy by duration of need. This description can then inform the duration of service needed from limited energy storage resources to reduce the probability of load being unserved. We first use a Bayesian additive model to infer the relationship between demand and available capacity as function of temperature. We then calculate the probability for when demand will be greater than supply for each unit increment of temperature. This probability can be described as a binomial random variable of demand being greater than supply for that hour. Finally, we estimate the duration of need by approximating the sum of binomial random variables for the day. With this methodology, one can rapidly simulate various supply mixes by fuel type to understand its effects on the final duration of need.