Fast Frequency Response Provision from Commercial Demand Response, from Scheduling to Stability in Power Systems

Misaghian, M. Saeed; Li, Ran; Flynn, Damian

doi:10.1049/icp.2021.1366

Cited by 4 publications

(4 citation statements)

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“…2) Renewable energy sources Ireland, as part of the EU, sets a 70% renewable electricity target for 2030, with a projection of ∼ 30% of final energy use contributing to electricity [48], but this transition in the electricity network from a generator dominated power system where conventional generating units mostly govern the system, toward an inverter dominated power system, where RES contributes significantly to the system raises issues concerning power system control [49]. Lessons have therefore been learned within the COVID-19 pandemic which applies to 2030 targets, specifically when it comes to wind curtailment, i.e.…”

Section: A Data Analyticsmentioning

confidence: 99%

“…By 2030, EiGrid and SONI (transmission system operator of Northern Ireland) need to operate the network and market with high levels of renewable generation yet with minimal unplanned curtailment. Potential solutions for reducing the wind curtailment, could come from demand side flexibility from data centres [30] and commercial/industrial loads [49]. Moreover, a new interconnector between Ireland and the northern transmission network of France called the "Celtic Interconnector" which would be online by 2030 could help this issue [50].…”

Section: A Data Analyticsmentioning

confidence: 99%

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Human Mobility-Based Features to Analyse the Impact of COVID-19 on Power System Operation of Ireland

Zarbakhsh

Misaghian

McArdle

2022

IEEE Open J. Power Energy

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are changing human mobility patterns; however, the effects on power systems remain unclear. Previous loads and timings along with weather features are often used in literature as input features in load forecasting, but these may be insufficient during COVID-19. As a result, this paper proposes an analytical framework to assess the impact of COVID-19 on power system operation as well as day-ahead electricity prices in Ireland. To improve peak demand forecasting during pandemics, we incorporate mobility, NPIs, and COVID-19 cases as complementary input features and representative of human behaviour changes. By defining different combinations of these explanatory features, several Machine Learning (ML) algorithms are applied and their performance is compared with the baseline scenario currently used in the literature. Using SHapley Additive Explanations (SHAP), we interpret the best performing model, Light Gradient Boosted Machine, to determine the influence of each feature on the predicted outcomes. We discover that typical load forecasting features still influence ML outcomes the most, but mobility-related changes are also significant. Our finding shows that NPIs impact human behaviour and electricity consumption during times of crisis and can be used in the context of load forecasting to assist policymakers and energy distributors.

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Section: A Data Analyticsmentioning

confidence: 99%

Section: A Data Analyticsmentioning

confidence: 99%

Human Mobility-Based Features to Analyse the Impact of COVID-19 on Power System Operation of Ireland

Zarbakhsh

Misaghian

McArdle

2022

IEEE Open J. Power Energy

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“…In summary, the current paper represents an extended version of previous work, presented at the IET RPG conference [21], which demonstrated the potential role of supermarket refrigeration systems in providing FFR for the Irish power system. The supermarket refrigeration system model is now presented in more detail, where dynamic changes associated with mass flow of the refrigerant and internal temperatures are discussed.…”

Section: Introductionmentioning

confidence: 96%

“…It has also been demonstrated that battery energy storage systems can be deployed on industrial manufacturing sites, equipped with combined heat and power units, to unlock their potential to provide a firm frequency response (<10 s) [20]. The ability of commercial refrigeration systems to provide FFR services in the Irish power system has also highlighted the benefits of a suitably fast control strategy [21]. In addition to FFR provision, commercial/industrial DR units can also contribute to other system services to increase system operational flexibility.…”

Section: Introductionmentioning

confidence: 99%

Fast frequency response provision from commercial demand response, from scheduling to stability in power systems

Misaghian

O’Dwyer

Flynn

2022

IET Renewable Power Gen

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Reduced numbers of online conventional generators, due to increasing shares of renewable energy sources, are increasing the requirement for a fast frequency response capability from reserve providers when a large infeed/outfeed trips. The commercial demand‐side sector, considering its size (MW/MWh) and existing communication and control infrastructure, makes it a potential valuable source of flexibility provision. Supermarket refrigeration systems are considered here as being representative of commercial end‐uses, with individual devices stochastically simulated under different ambient conditions, supermarket occupancy, food categories, and refrigeration performance. An aggregated fleet of supermarket loads are then created to provide a system‐wide reserve response. The all‐island power system of Northern Ireland and Republic of Ireland, with a horizon of 2030, is considered as a representative case study. Three distinct demand‐side frequency control methods, including static, droop, and hybrid control are presented and developed within a stability analysis platform. Year‐long simulation results show a general improvement in the frequency nadir and (maximum) rate of change of frequency when supermarkets are providing fast frequency response services to the grid, with the likelihood of severe transients (frequency nadir <49 Hz) greatly reduced (82% improvement), depending on the FFR control design, and associated speed of response.

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Artificial Intelligence Application in Demand Response: Advantages, Issues, Status, and Challenges

et al. 2023

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In recent years, there has been a significant growth in demand response (DR) as a cost-effective technique of providing flexibility and, as a result, improving the dependability of energy systems. Although the tasks associated with demand side management (DSM) are extremely complex, the use of large-scale data and the frequent requirement for near-real-time decisions mean that Artificial Intelligence (AI) has recently emerged as a key technology for enabling DSM. Optimization algorithm methods can be used to address a variety of problems, including selecting the optimal set of consumers to respond to, learning their attributes and preferences, dynamic pricing, device scheduling, and control, as well as determining the most effective way to incentive and reward participants in DR schemes fairly and effectively. The implementation optimization algorithm needs proper selection to mitigate the cost of energy consumption. Due to that reason, this paper outlines various challenges and opportunities in developing, utilizing, controlling, and scheduling the DR scheme's optimization algorithm. In addition, several issues in applications and advantages of optimization techniques in artificial intelligence approaches are discussed. The importance of implementing demand response mechanisms in developing countries is also presented. In addition, the status of demand response optimization in demandside management solutions is also illustrated congruently.

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Fast Frequency Response Provision from Commercial Demand Response, from Scheduling to Stability in Power Systems

Cited by 4 publications

References 0 publications

Human Mobility-Based Features to Analyse the Impact of COVID-19 on Power System Operation of Ireland

Human Mobility-Based Features to Analyse the Impact of COVID-19 on Power System Operation of Ireland

Fast frequency response provision from commercial demand response, from scheduling to stability in power systems

Artificial Intelligence Application in Demand Response: Advantages, Issues, Status, and Challenges

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