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Register a new userChallenges and opportunities of inertia estimation and forecasting in low-inertia power systems
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Accurate inertia estimates and forecasts are crucial to support the system operation in future low-inertia power systems. A large literature on inertia estimation methods is available. This paper aims to provide an overview and classification of inertia estimation methods. The classification considers the time horizon the methods are applicable to, i.e., offline post mortem, online real time and forecasting methods, and the scope of the inertia estimation, e.g., system-wide, regional, generation, demand, individual resource. Shortcomings of the existing inertia estimation methods have been identified and suggestions for future work have been made.
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Most renewable energy sources (RES) do not provide any inertial response. Their integration in a power grid implies a highly reduced level of system inertia, which leads to a deteriorated frequency performance. Then, the requirement for frequency response is significantly increased in order to maintain frequency security. Alternatively, enhanced provision of inertia from auxiliary sources may alleviate this problem. However, the benefits of inertia provision are not yet fully understood. In this paper, an inertia-dependent Stochastic Unit Commitment (SUC) tool is applied to quantify the economic value of inertia. The results demonstrate that enhanced provision of inertia would lead to significant economic savings, although these savings vary under different system conditions. These results should be brought to the attention of both market operators and investors, in order to inform the design of an ancillary-services market for inertia and the investment in auxiliary provision of inertia.
A significant amount of converter-based generation is being integrated into the bulk electric power grid to fulfill the future electric demand through renewable energy sources, such as wind and photovoltaic. The dynamics of converter systems in the overall stability of the power system can no longer be neglected as in the past. Numerous efforts have been made in the literature to derive detailed dynamic models, but using detailed models becomes complicated and computationally prohibitive in large system level studies. In this paper, we use a data-driven, black-box approach to model the dynamics of a power electronic converter. System identification tools are used to identify the dynamic models, while a power amplifier controlled by a real-time digital simulator is used to perturb and control the converter. A set of linear dynamic models for the converter are derived, which can be employed for system level studies of converter-dominated electric grids.
The reduced inertia levels in low-carbon power grids necessitate explicit constraints to limit frequencys nadir and rate of change during scheduling. This can result in significant curtailment of renewable energy due to the minimum generation of thermal plants that are needed to provide frequency response (FR) and inertia. Additional consideration of fast FR, a dynamically reduced largest loss and under frequency load shedding (UFLS) allows frequency security to be achieved more cost effectively. This paper derives a novel nadir constraint from the swing equation that, for the first time, provides a framework for the optimal comparison of all these services. We demonstrate that this constraint can be accurately and conservatively approximated for moderate UFLS levels with a second order cone, resulting in highly tractable convex problems. Case studies performed on a Great Britain 2030 system demonstrate that UFLS as an option to contain single plant outages can reduce annual operational costs by up to {pounds}559m, 52% of frequency security costs. The sensitivity of this value to wind penetration, abundance of alternative frequency services, UFLS amount and cost is explored.
Renewable-dominant power systems explore options to procure virtual inertia services from non-synchronous resources (e.g., batteries, wind turbines) in addition to inertia traditionally provided by synchronous resources (e.g., thermal generators). This paper designs a stochastic electricity market that produces co-optimized and efficient prices for energy, reserve and inertia. We formulate a convex chance-constrained stochastic unit commitment model with inertia requirements and obtain equilibrium energy, reserve and inertia prices using convex duality. Numerical experiments on an illustrative system and a modified IEEE 118-bus systems show the performance of the proposed pricing mechanism.
A major concern associated to the massive connection of distributed energy resources is the increasing share of power electronic interfaces resulting in the global inertia reduction of power systems. The recent literature advocated the use of voltage source converter (VSC) interfaced battery energy storage system (BESS) as a potential way to counterbalance this lack of inertia. However, the impact of VSCs on the dynamics of reduced-inertia grids is not well understood especially with respect to large transmission grids interfacing a mix of rotating machines and resources interfaced with power electronics. In this regards, we propose an extension of the IEEE 39-bus test network used to quantify the impact of VSCs on reduced-inertia grids. In this respect, a reduced-inertia 39-bus system is obtained by replacing 4 synchronous generators in the original 10-synchronous machine system, with 4 wind power plants modeled as aggregated type-3 wind turbines. Then, a large-scale BESS is integrated into the reduced-inertia network via a three-level neutral-point clamped (NPC) converter, thereby to be used for studying the impact of VSC on the dynamics of the inertia-reduced power system, as well as for comparing different VSC controls. The proposed models are implemented on a real-time simulator to conduct post-contingency analysis, respectively, for the original power system and the reduced-inertia one, with and without the BESS-VSC.
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