High penetration of wind generation will increase the requirement for fast frequency response services as currently wind plants do not provide inertial response. Although the importance of inertia reduction has been widely recognized, its impact on the system scheduling has not been fully investigated. In this context, this paper proposes a novel mixed integer linear programming (MILP) formulation for stochastic unit commitment that optimizes system operation by simultaneously scheduling energy production, standing/spinning reserves and inertia-dependent fast frequency response in light of uncertainties associated with wind production and generation outages. Post-fault dynamic frequency requirements, rate of change of frequency, frequency nadir and quasi-steady-state frequency are formulated as MILP constraints by using the simplified model of system dynamics. Moreover the proposed methodology enables the impact of wind uncertainty on system inertia to be considered. Case studies are carried out on the 2030 Great Britain system to demonstrate the importance of incorporating inertia-dependent fast frequency response in the stochastic scheduling and to indicate the potential for the proposed model to inform reviews of grid codes associated with fast frequency response and future development of inertia-related market.

Thermostatically controlled loads (TCLs), such as refrigerators, air-conditioners and space heaters, offer significant potential for short-term modulation of their aggregate power consumption. This ability can be used in principle to provide frequency response services, but controlling a multitude of devices to provide a measured collective response has proven to be challenging. Many controller implementations struggle to manage simultaneously the short-term response and the long-term payback, whereas others rely on a real-time command-and-control infrastructure to resolve this issue. In this paper, we propose a novel approach to the control of TCLs that allows for accurate modulation of the aggregate power consumption of a large collection of appliances through stochastic control. By construction, the control scheme is well suited for decentralized implementation, and allows each appliance to enforce strict temperature limits. We also present a particular implementation that results in analytically tractable solutions both for the global response and for the device-level control actions. Computer simulations demonstrate the ability of the controller to modulate the power consumption of a population of heterogeneous appliances according to a reference power profile. Finally, envelope constraints are established for the collective demand response flexibility of a heterogeneous set of TCLs.

Future changes in the generation mix may reduce the overall amount of system inertia. It becomes crucial to provide response services more quickly to secure post-fault transient frequency dynamics. This paper proposes a novel mixed integer linear programming unit commitment formulation that simultaneously optimizes energy production and the allocation of inertial response (IR), enhanced frequency response (EFR) and primary response (PR) against largest plant outage. A set of linearized inertia-dependent and multi-speed constraints on frequency evolution explicitly recognizes the quicker provision of EFR compared to PR and their mutual interplay with IR. The proposed model is applied to analyze a typical 2030 GB low-carbon scenario. Results demonstrate the value, as reduction of system operational cost, when battery storage provides EFR, facilitating a cost-efficient transition toward the low-carbon electricity sector.

In the Great Britain power system, reduced system inertia (particularly during low demand conditions) and larger possible infeed loss would make grid frequency regulation extremely challenging in future. Traditional primary frequency response could be insufficient to limit the frequency variation within acceptable range. This paper shows that thermostatically controlled loads (TCLs) (domestic refrigerators) can be controlled without real-time communication and in a nondisruptive way to collectively enhance the network frequency response. The aggregated power consumption of TCLs, distributed across the system, could be controlled as a `linear' function of the locally measured frequency and its rate of change. Alternatively, their aggregated consumption could be made to follow a `pre-set' power profile depending on the estimated infeed loss. A novel technique for accurate estimation of infeed loss and consequent postfault TCL power reduction is also proposed. The effectiveness of the two TCL control strategies is compared for primary and secondary frequency response through a case study on a 36 busbar reduced equivalent of the Great Britain power system. The effect of spatial variation of transient frequencies and the time delays in frequency measurement and filtering are considered to show how the TCLs can realistically provide rapid frequency response.

This paper proposes a novel distributed solution for the operation of large populations of thermostatically controlled loads (TCLs) providing frequency support. A game-theory framework is adopted, modelling the TCLs as price-responsive rational agents that schedule their energy consumption and allocate frequency response provision in order to minimize their operational costs. The novelty of this work lies in the use of mean field games to abstract the complex interactions of large numbers of TCLs with the grid and in the introduction of an innovative market structure, envisioning distinct price signals for electricity and response. Differently from previous approaches, such prices are not designed ad hoc but are derived instead from an underlying system scheduling model.

Collectively, thermostatically controlled loads (TCLs) offer significant potential for short‐term demand response. This intrinsic flexibility can be used to provide various ancillary services or to carry out energy arbitrage. This study introduces an aggregate description of the flexibility of a heterogeneous TCL as a leaky storage unit , with associated constraints that are derived from the TCL device parameters and quality of service requirements. In association with a suitable TCL control strategy this enables a straightforward embedding of TCL dynamics in optimisation frameworks. The tools developed are applied to the problem of determining an optimal multi‐service portfolio for TCLs. A linear optimisation model is constructed for the optimal simultaneous allocation of frequency services and energy arbitrage. In a case study, optimal service allocations are computed for eight representative classes of cold appliances and the results are validated using simulations of individual refrigerators. Finally, it is demonstrated that clustering of appliances with similar capabilities can significantly enhance the flexibility available to the system.