New generators located far from consumption centers require transmission infrastructure and increase network losses. The primary objective of this paper is to study signals that affect the location of generation investment. Such signals result from the electricity market itself and from additional regulatory instruments. We cluster them into five groups: locational electricity markets, deep grid connection charges, grid usage charges, capacity mechanisms, and renewable energy support schemes. We review the use of instruments in twelve major power systems and discuss relevant properties, including a quantitative estimate of their strength. We find that most systems use multiple instruments in parallel, and none of the identified instruments prevails. The signals vary between locations by up to 20 EUR per MWh. Such a difference is significant when compared to the levelized costs of combined cycle plants of 64-72 EUR per MWh in Europe.

Numerical optimization models are used to develop scenarios of the future energy system. Usually, they optimize the energy mix subject to engineering costs such as equipment and fuel. For onshore wind energy, some of these models use cost-potential curves that indicate how much electricity can be generated at what cost. These curves are upward sloping mainly because windy sites are occupied first and further expanding wind energy means deploying less favorable resources. Meanwhile, real-world wind energy expansion is curbed by local resistance, regulatory constraints, and legal challenges. This presumably reflects the perceived adverse effect that onshore wind energy has on the local human population, as well as other negative external effects. These disamenity costs are at the core of this paper. We provide a comprehensive and consistent set of cost-potential curves of wind energy for all European countries that include disamenity costs, and which can be used in energy system modeling. We combine existing valuation of disamenity costs from the literature that describe the costs as a function of the distance between turbine and households with gridded population data, granular geospatial data of wind speeds, and additional land-use constraints to calculate such curves. We find that disamenity costs are not a game changer: for most countries and assumptions, the marginal levelized cost of onshore wind energy increase by 0.2–12.5 €/MWh.

Solar energy supplies increasing shares of global energy demand. As a renewable source of energy, it will play a major role in decarbonizing electricity supply. This chapter provides an overview on the solar sector from an economic perspective. It describes the technical characteristics of photovoltaic and concentrated solar power and explains how these affect the economic competitiveness of solar energy. The authors highlight trends in the solar sector and elaborate on how this intermittent source of energy can be integrated into a power system. They conclude with a discussion on how renewable energy support schemes can be designed to foster the deployment of solar power by accounting for the specific characteristics of solar power.

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<p>Numerical optimization models are used to develop scenarios of the future energy system. Usually, they optimize the energy mix subject to engineering costs such as equipment and fuel. For onshore wind energy, some of these models use cost-potential curves that indicate how much electricity can be generated at what cost. These curves are upward sloping mainly because windy sites are occupied first and further expanding wind energy means deploying less favorable resources. Meanwhile, real-world wind energy expansion is curbed by local resistance, regulatory constraints, and legal challenges. This presumably reflects the perceived adverse effect that onshore wind energy has on the local human population, as well as other negative external effects. These disamenity costs are at the core of this paper. We provide a comprehensive and consistent set of cost-potential curves of wind energy for all European countries that include disamenity costs, and which can be used in energy system modeling. We combine existing valuation of disamenity costs from the literature that describe the costs as a function of the distance between turbine and households with gridded population data, granular geospatial data of wind speeds, and additional land-use constraints to calculate such curves. We find that disamenity costs are not a game changer: for most countries and assumptions, the marginal levelized cost of onshore wind energy increase by 0.2–12.5 €/MWh.</p>