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Quantum computing is rapidly advancing, harnessing the power of qubits’ superposition and entanglement for computational advantages over classical systems. However, scalability poses a primary challenge for these machines. By implementing a hybrid workflow between classical and quantum computing instances, D-Wave has succeeded in pushing this boundary to the realm of industrial use. Furthermore, they have recently opened up to mixed integer linear programming (MILP) problems, expanding their applicability to many relevant problems in the field of optimisation. However, the extent of their suitability for diverse problem categories and their computational advantages remains unclear. This study conducts a comprehensive examination by applying a selection of diverse case studies to benchmark the performance of D-Wave’s hybrid solver against that of industry-leading solvers such as CPLEX, Gurobi, and IPOPT. The findings indicate that D-Wave’s hybrid solver is currently most advantageous for integer quadratic objective functions and shows potential for quadratic constraints. To illustrate this, we applied it to a real-world energy problem, specifically the MILP unit commitment problem. While D-Wave can solve such problems, its performance has not yet matched that of its classical counterparts.

In the framework of the Paris Agreement, the European Union (EU) will have to firmly set decarbonization targets to 2050. However, the viability on these targets is an ongoing discussion. The European Commission has made several propositions for energy and climate \"roadmaps\". In this regard, this paper contributes by analyzing alternative pathways derived in a unique modelling process. As part of the SET-Nav project, we defined four pathways to a clean, secure and efficient energy system—taking different routes. Two key uncertainties shape the SET-Nav pathways: the level of cooperation (i.e. cooperation versus entrenchment) and the level of decentralization (i.e. decentralization versus path dependency). All four pathways achieve an 85–95% emissions reduction by 2050. We include a broad portfolio of options under distinct framework conditions by comprehensively analyzing all energy-consuming and energy-providing sectors as well as the general economic conditions. We do this by applying a unique suite of linked models developed in the SET-Nav project. By linking more than ten models, we overcome the traditional limitation of models that cover one single sector while at the same time having access to detail sectoral data and expertise. In this paper, we focus on the implications for the energy demand sectors (buildings, transport, and industry) and the electricity supply mix in Europe and compare our insights of the electricity sector to the scenarios of the recent European Commission (2018a) report \"A clean Planet for all\".

About 7 % of the energy consumption in the UK presently comes from wind, but this is expected to grow to well over 20 %. This causes serious concerns about the ability of the energy system to balance supply and demand, as it is already very inflexible. Though each household is very small, in total they contribute substantially to the energy demand, and in particular to the peak demand. In this paper, we develop a bottom-up approach, focusing on the value of energy storage and renewable micro-generation in domestic homes. Specifically, we consider a connection to the grid, a boiler, a solar collector, a small wind turbine, a water tank, and a battery. We use the wholesale spot prices as proxies for the provision costs of gas and electricity. We focus on the predictable inter-temporal variations of energy demand, wind speed, and spot prices, and thus assume that these parameters, though deterministic, are time varying. The objective of the model is to minimize the total energy consumption cost, as seen from the grid, throughout a finite horizon. We conduct a numerical case study using a sample of real-life demand and weather data for some typical houses in the UK and recent spot price data. Our results show that a battery might have a significant contribution to energy cost savings, and shed new light on the design of distributed energy systems for a smart grid, especially when coupled with a wind turbine. The benefits do not depend on behavioural changes in the households.

The intermittent nature of wind energy generation has introduced a new degree of uncertainty to the tactical planning of energy systems. Short-term energy balancing decisions are no longer (fully) known, and it is this lack of knowledge that causes the need for strategic thinking. But despite this observation, strategic models are rarely set in an uncertain environment. And even if they are, the approach used is often inappropriate, based on some variant of scenario analysis—what-if analysis. In this paper we develop a deterministic strategic model for the valuation of electricity storage (a battery), and ask: “Though leaving out wind speed uncertainty clearly is a simplification, does it really matter for the valuation of storage?”. We answer this question by formulating a stochastic programming model, and compare its valuation to that of its deterministic counterpart. Both models capture the arbitrage value of storage, but only the stochastic model captures the battery value stemming from wind speed uncertainty. Is the difference important? The model is tested on a case from Lancaster University’s campus energy system where a wind turbine is installed. From our analysis, we conclude that considering wind speed uncertainty can increase the estimated value of storage with up to 50 % relative to a deterministic estimate. However, we also observe cases where wind speed uncertainty is insignificant for storage valuation.

Hydrogen will be a central cross-sectoral energy carrier in the decarbonization of the European energy system. This paper investigates how a large-scale deployment of green hydrogen production affects the investments in transmission and generation towards 2060, analyzes the North Sea area with the main offshore wind projects, and assesses the development of an offshore energy hub. Results indicate that the hydrogen deployment has a tremendous impact on the grid development in Europe and in the North Sea. Findings indicate that total power generation capacity increases around 50%. The offshore energy hub acts mainly as a power transmission asset, leads to a reduction in total generation capacity, and is central to unlock the offshore wind potential in the North Sea. The effect of hydrogen deployment on power prices is multifaceted. In regions where power prices have typically been lower than elsewhere in Europe, it is observed that hydrogen increases the power price considerably. However, as hydrogen flexibility relieves stress in high-demand periods for the grid, power prices decrease in average for some countries. This suggests that while the deployment of green hydrogen will lead to a significant increase in power demand, power prices will not necessarily experience a large increase.

Purpose of Review Balancing a large share of solar and wind power generation in the power system will require a well synchronized coordination of all possible flexibility sources. This entails developing market designs that incentivize flexibility providers, and define new flexibility products. To this end, the paper reviews latest trends in the characterization of flexibility by understanding its dimensions in terms of time, spatiality, resource type, and associated risks. Also, as aggregators have emerged as important actors to deliver, and to reward end-user flexibility, the paper reviews latest trends in the topic. Recent Findings The review reports latest trends and discussions on power system flexibility and their relations to market design. The current academic literature indicates that there are open question and limited research on how to reward short-term flexibility while considering its long-term economic viability. Demand-side flexibility through aggregation holds great potential to integrate renewables. Summary Research in power system flexibility has to put effort on analysing new time-structures of electricity markets and define new marketplaces that consider the integration of new flexibility products, actors (e.g. aggregators, end-users), and mechanisms (e.g. TSO-DSO coordination).

The widespread deployment of renewable energy in combination with smart grid technologies is creating the opportunity for energy storage to play a critical role in energy systems and be a more prevalent technology in the near future. This research adopts a bottom-up approach to model the interactions within the energy system, namely between renewable supply and storage technologies at the end-user level (e.g. in houses, buildings and communities). That is, the papers presented in this thesis investigate the value of energy storage in smart grids from an end-user perspective. Specifically, it focuses on the role of storage units in the regulation of the energy system’s supply-demand balance and the cost savings for the end-user. Hence, the contribution of this study lies in the perspective taken (end-user), as well as in the models developed for the valuation of energy storage. In this sense, it provides an understanding of different aspects associated with the valuation of storage technologies in smart grids. Particular attention is paid to the contribution of local wind energy supply as well as demand response (smart grids). Analytics data-driven optimization is central in this research.To analyze the interactions on complementing energy storage with decentralized energy system in smart grids, this thesis develops models for two real-life case studies: The first paper assess the value of batteries in domestic houses in sync with on-site renewable micro generation. A second paper extends the research, bottom-up, to a community dependent on its own hybrid decentralized generation coupled with energy storage units. As uncertainty of wind generation is not considered in these cases, a third paper investigates the storage valuation by a stochastic programming approach.

Quantum computing is rapidly advancing, harnessing the power of qubits' superposition and entanglement for computational advantages over classical systems. However, scalability poses a primary challenge for these machines. By implementing a hybrid workflow between classical and quantum computing instances, D-Wave has succeeded in pushing this boundary to the realm of industrial use. Furthermore, they have recently opened up to mixed integer linear programming (MILP) problems, expanding their applicability to many relevant problems in the field of optimisation. However, the extent of their suitability for diverse problem categories and their computational advantages remains unclear. This study conducts a comprehensive examination by applying a selection of diverse case studies to benchmark the performance of D-Wave's hybrid solver against that of industry-leading solvers such as CPLEX, Gurobi, and IPOPT. The findings indicate that D-Wave's hybrid solver is currently most advantageous for integer quadratic objective functions and shows potential for quadratic constraints. To illustrate this, we applied it to a real-world energy problem, specifically the MILP unit commitment problem. While D-Wave can solve such problems, its performance has not yet matched that of its classical counterparts.

Thorsten Burandt, Pedro Crespo del Granado, and Ruud Egging apply a multi-sectoral energy system model to analyze the energy transition with ambitious decarbonization scenarios. Results show that significant amounts of gas-fired capacity might end up stranded. Introduction of capacity markets and using biogas, synthetic methane, or hydrogen instead of natural gas, can reduce the risk of stranded assets.

As the European countries strive to meet their ambitious climate goals, renewable hydrogen has emerged to aid in decarbonizing energy-intensive sectors and support the overall energy transition. To ensure that hydrogen production aligns with these goals, the European Commission has introduced criteria for additionality, temporal correlation, and geographical correlation. These criteria are designed to ensure that hydrogen production from renewable sources supports the growth of renewable energy. This study assesses the impact of these criteria on green hydrogen production, focusing on production costs and technology impacts. The European energy market is simulated up to 2048 using stochastic programming, applying these requirements exclusively to green hydrogen production without the phased-in compliance period outlined in the EU regulations. The findings show that meeting the criteria will increase expected system costs by 82 billion euro from 2024 to 2048, largely due to the rapid shift from fossil fuels to renewable energy. The additionality requirement, which mandates the use of new renewable energy installations for electrolysis, proves to be the most expensive, but also the most effective in accelerating renewable energy adoption.