• Energy Research

Ireland has ambitious greenhouse gas reduction targets for 2030, with a range of measures being proposed to reduce fossil fuel consumption in various energy sectors through electrification. This article analyses the costs and benefits of several sector-coupling measures using an energy system model with hourly scheduling of the whole energy system. The results indicate the potential importance of electricity based heating in the industrial sector, smart charging of electric vehicles, batteries and power-to-ammonia, as part of achieving the future targets.

AbstractAs power systems evolve towards integrating higher shares of renewables, the demand for additional levels of flexibility is increased. Meanwhile, o‐ther energy consuming sectors, such as transport and heating, could provide flexibility when they move from fossil fuels to electricity. In this paper, the impact of a range of flexibility measures is assessed for the island system of Ireland, with a high share of renewable energy, particularly wind and solar. Flexibility measures studied include hybrid heating in domestic and industrial processes, smart charging of electric vehicles, renewable hydrogen, power to ammonia, peak shaving demand response and batteries. The novelty of this paper lies in directly quantifying the interactions and dependencies between different flexibility measures, with the objective of increasing the operational flexibility of an increasingly renewable energy‐dominated power system. Four different scenarios are modeled to explore this interplay between the different flexibility measures. The costs and benefits of several sector‐coupling measures. The scenarios have also been compared in terms of their influence on system inertia, renewable energy curtailment and non‐synchronous penetration levels. The results indicate the potential importance of electricity‐based heating in the industrial sector, smart charging of electric vehicles, batteries and power‐to‐ammonia, as part of achieving future targets

One of the most promising ways of de-carbonising the energy sector is through increasing the amounts of inherently uncertain variable renewable energy (VRE) generation in power systems.Typically, stochastic energy system studies have focused solely on the day-ahead horizon of 36 hours ahead of time, while studies about hydro-thermal scheduling and expansion planning often neglect VRE uncertainty entirely. In this work, the potential benefits of extending the horizon of VRE forecasts on the operation of hydro-dominated power systems was studied using a future Nordic system case study. 15-day ensemble weather forecasts were processed into realistic VRE forecasts up to 348 hours ahead of time, and their impacts on power system operations were simulated using stochastic unit commitment and economic dispatch optimisation. Increasing the length of the modelled forecast horizon reduced the total yearly operational costs of the system by 0.18–0.41%, as well as the spillage of run-of-river hydropower and the curtailment of wind power by 0.42–0.47 and 0.05–0.07% points respectively.

As the share of variable generation in power systems increases, there is increasing value in more flexible use and generation of electricity. The paper compares the economic value of several flexibility options in a large power system with a large amount of reservoir hydro power. Generation planning models are needed to consider the impact of flexibility options on other investments in a power system. However, generation planning models do not include all the relevant operational details. The approach in the paper combines a generation planning model with a unit commitment and dispatch model. The results demonstrate the value of coupling the heat and power sectors and the value of transmission. Low-cost electricity storage does not appear to be as decisive in the Northern European context with wind power as the main variable generation source. The paper also addresses methodological issues related to the inclusion of operational constraints in generation planning.

Backbone represents a highly adaptable energy systems modelling framework, which can be utilised to create models for studying the design and operation of energy systems, both from investment planning and scheduling perspectives. It includes a wide range of features and constraints, such as stochastic parameters, multiple reserve products, energy storage units, controlled and uncontrolled energy transfers, and, most significantly, multiple energy sectors. The formulation is based on mixed-integer programming and takes into account unit commitment decisions for power plants and other energy conversion facilities. Both high-level large-scale systems and fully detailed smaller-scale systems can be appropriately modelled. The framework has been implemented as the open-source Backbone modelling tool using General Algebraic Modeling System (GAMS). An application of the framework is demonstrated using a power system example, and Backbone is shown to produce results comparable to a commercial tool. However, the adaptability of Backbone further enables the creation and solution of energy systems models relatively easily for many different purposes and thus it improves on the available methodologies.