• Energy Research

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.

Abstract The dynamic integration of building energy models and power system models is essential to analyse the potential supply-side benefits of adopting Energy Conservation Measures (ECM) at the national, regional scale. The integration of these models requires the development of linear building energy models which are numerically compatible with linear power systems models. Ensemble Calibration is an automated model calibration methodology which identifies a cluster of lumped parameter building energy models (denoted Ensemble models) using an archetype building energy model. Each lumped parameter building energy model represents an ECM configuration (i.e., a combination of ECMs). The current paper introduces a novel mechanism by which Ensemble models are integrated with power systems models in a manner such that cost-optimal retrofit decision-making problems and power systems optimisation problems can be simultaneously solved. To achieve this objective, Ensemble models are reformulated as bi-linear models, which are then co-optimised with power systems models using a multi-stage optimisation algorithm. The methodology is tested using two building energy archetypes: a detached house archetype and a mid-floor apartment archetype. The archetypes are representative of the Irish residential stock built prior to 1970. The results show that the proposed multi-stage optimisation algorithm provides computational advantages to the solution of the equivalent problem using a brute force approach (i.e., solving building-to-grid models for each ECM configuration). The proposed method is 4.73 times faster than the brute force approach when the archetypes are decoupled and 73 times faster when the models are deemed to be coupled via a power systems model.

Future low-carbon systems with very high shares of variable renewable generation require complex models to optimise investments and operations, which must capture high degrees of sector coupling, contain high levels of operational and temporal detail, and when considering seasonal storage, be able to optimise both investments and operations over long durations. Standard energy system models often do not adequately address all these issues, which are of great importance when considering investments in emerging energy carriers such as Hydrogen. An advanced energy system model of the Irish power system is built in SpineOpt, which considers a number of future scenarios and explores different pathways to the wide-scale adoption of Hydrogen as a low-carbon energy carrier. The model contains a high degree of both temporal and operational detail, sector coupling, via Hydrogen, is captured and the optimisation of both investments in and operation of large-scale underground Hydrogen storage is demonstrated. The results highlight the importance of model detail and demonstrate how over-investment in renewables occur when the flexibility needs of the system are not adequately captured. The case study shows that in 2030, investments in Hydrogen technologies are limited to scenarios with high fuel and carbon costs, high levels of Hydrogen demand (in this case driven by heating demand facilitated by large Hydrogen networks) or when a breakthrough in electrolyser capital costs and efficiencies occurs. However high levels of investments in Hydrogen technologies occur by 2040 across all considered scenarios. As with the 2030 results, the highest level of investments occur when demand for Hydrogen is high, albeit at a significantly higher level than 2030 with increases in investments of large-scale electrolysers of 538%. Hydrogen fuelled compressed air energy storage emerges as a strong investment candidate across all scenarios, facilitating cost effective power-to-Hydrogen-to-power conversions.

High net load variability, driven by high penetrations of wind and solar generation, will create challenges for system operators in the future, as installed wind generation capacities increase to unprecedented levels globally. Maintaining system reliability, particularly at shorter time-scales, leads to increased levels of conventional plant starts and ramping, and higher levels of wind curtailment, with sub-hourly unit commitment and economic dispatch required to capture the increased cycling burden. The role of energy storage in reducing operating costs and enhancing system flexibility is explored, with key storage plant characteristics for balancing at this time-scale identified and discussed in relation to existing and emerging grid-scale storage technologies. Unit dispatches for the additional storage plant with varying characteristics highlight the unsuitability of energy only markets in incentivizing suitable levels of flexibility for future systems with high net load variability.

Future power systems with high penetrations of variable renewables will require increased levels of flexibility from generation and demand-side sources in order to maintain secure and stable operation. One potential flexibility source is large-scale energy storage, which can provide a variety of ancillary services across multiple time scales. In order for appropriate levels of investment to take place, and in order for existing assets to be utilized optimally, it is essential that market signals are present which encourage suitable levels of flexibility, either from storage or alternative sources. Suboptimal storage plant dispatch due to uncertainty and inefficient market incentives are represented as operational constraints on the storage plant, and the impact of these inefficiencies are highlighted. Thus, changes required in operational practices for storage plant at different installed wind capacity levels, and the challenges that private storage plant operators will face in generating appropriate bids in a market environment at high variable renewable penetrations are explored. The impacts on system generating costs and storage profits are explored under different plant operating assumptions.

Planning of future energy systems with higher prevalence of wind and solar energy requires a careful representation of the temporal and operational characteristics of the system in the investment planning model. This study aims to identify the aspects that should be considered when selecting the representation for a particular system. To demonstrate the impacts that various model representations have in terms of model accuracy and computational effort, we carry out case studies on two test systems implemented within the Backbone energy systems modelling framework. The results show that the temporal and operational representations have different benefits and weaknesses in different system types. The findings provide general guidelines on the relative importance of different model details, depending on the characteristics of the system under study. For example, some temporal sampling strategies can better capture long-term storage needs, while others are more suitable for short-term storage modelling. Likewise, solar-dominated and wind-dominated systems differ in their methodological requirements. Furthermore, the interactions between energy sectors and the operational limits of the technologies for sector coupling should be correctly captured, as they significantly impact on the value of different technologies and their flexibility. Finally, we recommend testing several temporal and technical representations for each particular system in order to ensure the feasibility of the selected method for that purpose. The findings and recommendations inform energy system modellers about improvements that will facilitate higher quality planning results.

Abstract The increase of renewable sources in the power sector is an important step towards more sustainable electricity production. However, introducing high shares of variable renewables, such as wind and solar, cause dispatchable power plants to vary their output to fulfill the remaining electrical demand. The environmental impacts related to potential future energy systems in Ireland for 2025 with high shares of wind power were evaluated using life cycle assessment (LCA), focusing on cycling emissions (due to part-load operation and start-ups) from dispatchable generators. Part-load operations significantly affect the average power plant efficiency, with all units seeing an average yearly efficiency noticeably less than optimal. In particular, load following units, on average, saw an 11% reduction. Given that production technologies are typically modeled assuming steady-state operation at full load, as part of LCA of electricity generation, the efficiency reduction would result in large underestimation of emissions, e.g. up to 65% for an oil power plant. Overall, cycling emissions accounted for less than 7% of lifecycle CO2, NOx and SO2 emissions in the five scenarios considered: while not overbalancing the benefits from increasing wind energy, cycling emissions are not negligible and should be systematically included (i.e. by using emission factors per unit of fuel input rather than per unit of power generated). As the ability to cycle is an additional service provided by a power plant, it is also recommended that only units with similar roles (load following, mid merit, or base load) should be compared. The results showed that cycling emissions increased with the installed wind capacity, but decreased with the addition of storage. The latter benefits can, however, only be obtained if base-load electricity production shifts to a cleaner source than coal. Finally, the present study indicates that, in terms of emission reductions, the priority for Ireland is to phase out coal-based power plants. While investing in new storage capacity reduces system operating costs at high wind penetrations and limits cycling, the emissions reductions are somewhat negated when coupled with base load coal.