You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
Given the increasing penetration in renewable generation, the UK power system is experiencing a decline in system inertia and an increase in frequency response (FR) requirements. Faster FR products are a mitigating solution that can cost-effectively meet the system balancing requirements. Thus, this paper proposes a mixed integer linear programming (MILP) unit commitment model which can simultaneously schedule inertial response, mandatory FR, as well as a sub-second FR product - enhanced frequency response (EFR). The model quantifies the value of providing faster reacting FR products in comparison with other response times from typical FR products. The performance and value of EFR are determined in a series of future energy scenarios with respect to the UK market and system conditions.

Cited Abstract(s):
[1]: How can the electricity system operator determine the optimal quantity and quality of electricity ancillary services (such as frequency response) to procure in a market increasingly characterized by intermittent renewable electricity generation? The paper presents a system operator’s utility function to calculate the exchange rates in monetary values between different frequency response products in the electricity system. We then use the utility function in a two-sided Vickrey-Clarke-Groves (VCG) mechanism combined of two frequency response products – enhanced and primary – in the context of the system in Great Britain. This mechanism would allow the market to reveal to the system operator the welfare optimal mix of speed of frequency response and quantity to procure. We show that this mechanism is the efficient way to support new faster sources of frequency response, such as could be provided by grid scale batteries.

[2]: Increase of penetration of intermittent renewable power connected to the system will increase the requirements for frequency regulation services. If these services are met by conventional plant running part-loaded, this will not only reduce the system operational efficiency but will also limit the ability of the system to accommodate renewable generation. This work quantifies the value of Dynamic Demand (DD) concept, which enables domestic refrigeration appliances to contribute to primary frequency regulation through an advanced stochastic control algorithm. The benefits of DD providing frequency response are determined for a wide range of future low-carbon generation systems, using an efficient generation scheduling model which includes scheduling of frequency regulation and reserve services. The analysis also considers the potential impact of wind generation on system inertia and primary frequency regulation. Simulations indicate that the benefits of DD increase considerably in systems with high wind penetration, making DD an attractive option for significantly improving system efficiency.

[3]: High penetration of renewable generation will increase the requirement for both operating reserve (OR) and frequency response (FR), due to its variability, uncertainty, and limited inertia capability. Although the importance of optimal scheduling of OR has been widely studied, the scheduling of FR has not yet been fully investigated. In this context, this paper proposes a computationally efficient mixed integer linear programming formulation for a full stochastic scheduling model that simultaneously optimizes energy production, OR, FR, and underfrequency load shedding. By using value of lost load (VOLL) as the single security measure, the model optimally balances the cost associated with the provision of various ancillary services against the benefit of reduced cost of load curtailment. The proposed model is applied in a 2030-GB system to demonstrate its effectiveness. The impact of installed capacity of wind generation and setting of VOLL is also analyzed.

[4]: Decarbonising electricity systems is essential for mitigating climate change. Future systems will likely incorporate higher penetrations of intermittent renewable and inflexible nuclear power. This will significantly impact on system operations, particularly the requirements for flexibility in terms of reserves and the cost of such services. This paper estimates the interrelated changes in wholesale electricity and reserve prices using two novel methods. Firstly, it simulates the short run marginal cost of generation using a unit commitment model with post-processing to achieve realistic prices. It also introduces a new reserve price model, which mimics actual operation by first calculating the day ahead schedules and then letting deviations from schedule drive reserve prices. The UK is used as a case study to compare these models with traditional methods from the literature. The model gives good agreement with and historic prices in 2015. In a 2035 scenario, increased renewables penetration reduces mean electricity prices, and leads to price spikes due to expensive plants being brought online briefly to cope with net load variations. Contrary to views previously held in literature, a renewable intensive scenario does not lead to a higher reserve price than a fossil fuel intensive scenario. Demand response technology is shown to offer sizeable economic benefits when maintaining system balance. More broadly, this framework can be used to evaluate the economics of providing reserve services by aggregating decentralised energy resources such as heat pumps, micro-CHP and electric vehicles.

[5]:
Previous works estimate FR requirements by adopting different methodologies, from pre-defined, generation-based requirements to inertia-dependent ones. In order to assess the economic benefits of dynamic demand in providing frequency regulation, [2] constructed a unit commitment (UC) model with per-defined frequency regulation and reserve requirements. Similarly, [4] developed a comprehensive UC model for the UK power system and used historical reserve requirements to estimate future electricity and reserve prices. System inertia can capture transient frequency evolution dynamics to accurately determine hourly-based FR requirements. Chavez2014a uses the system inertia to estimate PFR adequacy. Recent works [3] successfully conducted the stochastic scheduling for generation dispatch and ancillary services with inertia-dependent constraints. Regarding the multi-speed FRs, [5] formulates the model that can simultaneously allocate inertia responses (IR), PR and EFR, and evaluates the EFR effectiveness. Considering different inertia conditions, [1] proposed a utility function for system operator (SO) to choose multi-speed FR products.