This chapter analyses the role that complementarity may play in renewable power generation asset siting decisions and its impact on power system design and economics. To this end, a two-stage approach is employed. In the first stage, a siting method is used to select a pre-specified number of sites optimizing a pre-defined siting criterion. In the second stage, the set of sites identified in the first stage is passed to a joint generation-transmission-storage capacity expansion planning model, which identifies the optimal power system configuration. Seven different siting criteria adapted from the literature are analyzed in a case study focusing on the deployment of onshore wind power plants in the European power system. The set of locations selected by the scheme that minimizes the average residual demand happens to coincide with that of the scheme maximizing the electricity output, which also yields the cheapest system design. On the other hand, the schemes minimizing the variability of the residual demand and the correlation between wind regimes were between 20% and 25% more expensive. The code and data enabling others to reproduce these results are also made available to the community.

This paper provides a detailed account of the impact of different offshore wind siting strategies on the design of the European power system. To this end, a two-stage method is proposed. In the first stage, a highly-granular siting problem identifies a suitable set of sites where offshore wind plants could be deployed according to a pre-specified criterion. Two siting schemes are analysed and compared within a realistic case study. These schemes essentially select a pre-specified number of sites so as to maximize their aggregate power output and their spatiotemporal complementarity, respectively. In addition, two variants of these siting schemes are provided, wherein the number of sites to be selected is specified on a country-by-country basis rather than Europe-wide. In the second stage, the subset of previously-identified sites is passed to a capacity expansion planning framework that sizes the power generation, transmission and storage assets that should be deployed and operated in order to satisfy pre-specified electricity demand levels at minimum cost. Results show that the complementarity-based siting criterion leads to system designs which are up to 5% cheaper than the ones relying on the power output-based scheme when offshore wind plants are deployed with no consideration for country-based deployment targets. On the contrary, the power output-based scheme leads to system designs which are consistently 2% cheaper than the ones leveraging the complementarity-based siting strategy when such constraints are enforced. The robustness of the reported results is supported by a sensitivity analysis on offshore wind capital expenditure and inter-annual weather variability, respectively.

In 2013, the European Network of Transmission System Operators (TSOs) for electricity (ENTSO-E) created the Pan-European Climate Database (PECD), a tool that has underpinned most studies conducted by TSOs ever since. So far, the different versions of the PECD have used so-called modern-era ‘reanalysis’ products that represent a gridded amalgamation of historical conditions from observations. However, scientific evidence suggests, and recent European regulation requires, that power system adequacy studies should take climate change into account when estimating the future potential of variable renewable resources, such as wind, solar and hydro, and the impact of temperature on electricity demand. This paper explains the need for future climate data in energy systems studies and provides high-level recommendations for building a future-proof reference climate dataset for TSOs, not just in Europe, but also globally.

The idea behind this chapter is to provide teachers and students with material that can be used while studying renewable energy sources with special attention paid to their complementary characteristics. The questions and exercises included below refer to chapters presented in the book. In case of any questions, we provide the readers with contact details to chapters corresponding authors who would be happy in assisting you in case of any queries.

The accurate representation of variable renewable generation (RES, e.g., wind, solar PV) assets in capacity expansion planning (CEP) studies is paramount to capture spatial and temporal correlations that may exist between sites and impact both power system design and operation. However, it typically has a high computational cost. This paper proposes a method to reduce the spatial dimension of CEP problems while preserving an accurate representation of renewable energy sources. A two-stage approach is proposed to this end. In the first stage, relevant sites are identified via a screening routine that discards the locations with little impact on system design. In the second stage, the subset of relevant RES sites previously identified is used in a CEP problem to determine the optimal configuration of the power system. The proposed method is tested on a realistic EU case study and its performance is benchmarked against a CEP set-up in which the entire set of candidate RES sites is available. The method shows great promise, with the screening stage consistently identifying 90% of the optimal RES sites while discarding up to 54% of the total number of candidate locations. This leads to a peak memory reduction of up to 41% and solver runtime gains between 31% and 46%, depending on the weather year considered.

In recent years, the vast penetration of renewable energy sources has introduced a large degree of uncertainty into the power system, thus leading to increased trading activity in the continuous intra-day electricity market. In this paper, we propose an agent-based modeling framework to analyze the behavior and the interactions between renewable energy sources, consumers and thermal power plants in the European Continuous Intra-day (CID) market. Additionally, we propose a novel adaptive trading strategy that can be used by the agents that participate in CID market. The agents learn how to adapt their behavior according to the arrival of new information and how to react to changing market conditions by updating their willingness to trade. A comparative analysis was performed to study the behavior of agents when they adopt the proposed strategy as opposed to other benchmark strategies. The effects of unexpected outages and information asymmetry on the market evolution and the market liquidity were also investigated.

This paper studies the problem of siting renewable power generation assets using large amounts of climatological data while accounting for their spatiotemporal complementarity. The problem is cast as a combinatorial optimisation problem selecting a pre-specified number of sites so as to minimise the number of simultaneous low electricity production events that they experience relative to a pre-specified reference production level. It is shown that the resulting model is closely related to submodular optimisation and can be interpreted as generalising the well-known maximum coverage problem. Both deterministic and randomised algorithms are discussed, including greedy, local search and relaxation-based heuristics as well as combinations of these algorithms. The usefulness of the model and methods is illustrated by a realistic case study inspired by the problem of siting onshore wind power plants in Europe, resulting in instances featuring over ten thousand candidate locations and ten years of hourly-sampled meteorological data. The proposed solution methods are benchmarked against a state-of-the-art mixed-integer programming solver and several algorithms are found to consistently produce better solutions at a fraction of the computational cost. The physical nature of solutions provided by the model is also investigated, and all deployment patterns are found to be unable to supply a constant share of the electricity demand at all times. Finally, a cross-validation analysis shows that, except for an edge case, the model can successfully and reliably identify deployment patterns that perform well on previously unseen climatological data from historical data spanning a small number of weather years.

This paper studies the economics of carbon-neutral synthetic fuel production from renewable electricity in remote areas where high-quality renewable resources are abundant. To this end, a graph-based optimisation modelling framework directly applicable to the strategic planning of remote renewable energy supply chains is proposed. More precisely, a hypergraph abstraction of planning problems is introduced, wherein nodes can be viewed as optimisation subproblems with their own parameters, variables, constraints and local objective. Nodes typically represent a subsystem such as a technology, a plant or a process. Hyperedges, on the other hand, express the connectivity between subsystems. The framework is leveraged to study the economics of carbon-neutral synthetic methane production from solar and wind energy in North Africa and its delivery to Northwestern European markets. The full supply chain is modelled in an integrated fashion, which makes it possible to accurately capture the interaction between various technologies on an hourly time scale. Results suggest that the cost of synthetic methane production and delivery would be slightly under 150 €/MWh (higher heating value) by 2030 for a system supplying 10 TWh annually and relying on a combination of solar photovoltaic and wind power plants, assuming a uniform weighted average cost of capital of 7%. A comprehensive sensitivity analysis is also carried out in order to assess the impact of various techno-economic parameters and assumptions on synthetic methane cost, including the availability of wind power plants, the investment costs of electrolysis, methanation and direct air capture plants, their operational flexibility, the energy consumption of direct air capture plants, and financing costs. The most expensive configuration (around 200 €/MWh) relies on solar photovoltaic power plants alone, while the cheapest configuration (around 88 €/MWh) makes use of a combination of solar PV and wind power plants and is obtained when financing costs are set to zero.

This paper proposes a systematic framework to assess the complementarity of renewable resources over arbitrary geographical scopes and temporal scales which is particularly well-suited to exploit very large data sets of climatological data. The concept of critical time windows is introduced, and a spatio-temporal criticality indicator is proposed, consisting in a parametrised family of scalar indicators quantifying the complementarity between renewable resources in both space and time. The criticality indicator is leveraged to devise a family of optimisation problems identifying sets of locations with maximum complementarity under arbitrary geographical deployment constraints. The applicability of the framework is shown in a case study investigating the complementarity between the wind regimes in continental western Europe and southern Greenland, and its usefulness in a power system planning context is demonstrated. Besides showing that the occurrence of low wind power production events can be significantly reduced on a regional scale by exploiting diversity in local wind patterns, results highlight the fact that aggregating wind power production sites located on different continents may result in a lower occurrence of system-wide low wind power production events and indicate potential benefits of intercontinental electrical interconnections.

Spatiotemporal complementarity between variable renewable energy sources (RES) has received a great deal of attention in recent years. However, its value for power systems is still not properly understood. This research gap is tackled in the current work by evaluating the benefits of siting RES assets according to resource complementarity criteria. To this end, a two-stage method is employed. First, the complementarity between RES is assessed and the locations sets that maximize it are selected using an integer programming model. Subsequently, the outcome of the first stage is used within an expansion planning framework which identifies the optimal system design and serves as a basis for assessing the economic value of RES complementarity for power systems. The analysis is conducted on a realistic case study targeting the deployment of 450 GW of offshore wind in Europe. Results show that siting based on RES complementarity is particularly attractive when the power density of wind developments is relatively high and when the inter-annual variability of the underlying resource is accounted for. More specifically, such a siting strategy leads to yearly savings between 0.3 and 1.2 billion EUR compared with conventional schemes seeking to deploy generation capacity at the most productive locations.

This paper proposes an optimisation-based framework to tackle long-term centralised planning problems of multi-sector, integrated energy systems including electricity, hydrogen, natural gas, synthetic methane and carbon dioxide. The model selects and sizes the set of power generation, energy conversion and storage as well as carbon capture technologies minimising the cost of supplying energy demand in the form of electricity, hydrogen, natural gas or synthetic methane across the power, heating, transportation and industry sectors whilst accounting for policy drivers, such as energy independence, carbon dioxide emissions reduction targets, or support schemes. The usefulness of the model is illustrated by a case study evaluating the potential of sector coupling via power-to-gas and carbon capture technologies to achieve deep decarbonisation targets in the Belgian context. Results, on the one hand, indicate that power-to-gas can only play a minor supporting role in cross-sector decarbonisation strategies in Belgium, as electrolysis plants are deployed in moderate quantities whilst methanation plants do not appear in any studied scenario. On the other hand, given the limited renewable potential, post-combustion and direct air carbon capture technologies clearly play an enabling role in any decarbonisation strategy, but may also exacerbate the dependence on fossil fuels.

Current global environmental challenges require vigorous and diverse actions in the energy sector. One solution that has recently attracted interest consists in harnessing high-quality variable renewable energy resources in remote locations, while using transmission links to transport the power to end users. In this context, a comparison of western European and Greenland wind regimes is proposed. By leveraging a regional atmospheric model specifically designed to accurately capture polar phenomena, local climatic features of southern Greenland are identified to be particularly conducive to extensive renewable electricity generation from wind. A methodology to assess how connecting remote locations to major demand centres would benefit the latter from a resource availability standpoint is introduced and applied to the aforementioned Europe-Greenland case study, showing superior and complementary wind generation potential in the considered region of Greenland with respect to selected European sites.

This paper proposes an optimisation-based framework to tackle long-term centralised planning problems of integrated energy systems with bi-directional electricity-gas carriers coupling under various policy constraints. The framework is leveraged to gain insight into possible configurations of the future Belgian energy system, and identify the cost-optimal energy mix as well as short and long-term storage requirements to satisfy carbon dioxide emissions reductions and energy security targets. Results shed light on the economics of a transition to a low-carbon energy system and reveal the potential of power-to-gas and storage in gas form to help achieve ambitious emissions reduction goals.