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• We analyse the reserve procurement in a renewable-dominated power system. • We focus on when reserves are scheduled and compare three market designs. • A full co-optimization of energy and reserve (Model 3) is the most efficient system. • Reserve scheduled after the clearing of the energy market can be as efficient as Model 3. • Reserve scheduled before the clearing of the energy market is the least efficient system. This paper investigates different reserve issues inspired by the European situation, focusing on both the moment when reserves are procured and the degree of coordination among Transmission System Operators (TSOs) in that procurement. We present three scheduling models formulated as stochastic programs that represent the day-ahead energy market, the reserve procurement, and the real-time balancing market in a renewable-dominated power system. Two of the proposed models are inspired by reserve procurement mechanisms currently applied in Europe, where reserves are committed either before (Model 1) or after (Model 2) the clearing of the day-ahead energy market. Then, we use as benchmark a third model in which energy and reserve capacity are co-optimized (Model 3). In all models, we consider the procurement of both conventional and upward/downward reserves. We also assess the impact of these organizations on market participants' remuneration and test the impact of cross-border constraints as those applying in the European power system. The case study is based on the IEEE 24-node RTS, considering the uncertainty in renewable power production and demand. Our results show that Model 1 is the least efficient market design as it leads to a misallocation of the available capacity, while Model 2 becomes as efficient as Model 3 when the TSOs procure reserve in a coordinated way. Finally, a coordinated procurement of reserves reduces the system operating costs in all models. [ABSTRACT FROM AUTHOR]

The European-Union Emissions Trading System (EU-ETS) is a cap and trade scheme that requires the industries participating in the program to obtain allowances to cover their carbon emissions. Energy Intensive Industries claim that this system puts their European plants at an economics disadvantage compared to facilities located outside the EU. As a direct consequence, industries may relocate their production activities in unregulated countries, leading to the so-called carbon leakage effect. In order to curb this effect, several policies have been devised, including grandfathering of CO 2 allowances and border tax adjustment. This paper investigates the impact of these two policies on the cement sector, with a particular focus on the Italian market, particularly prone to carbon leakage. The analysis is based on an oligopolistic partial equilibrium model with a detailed technological representation of the market. The model is a Generalized Nash Equilibrium Problem that accounts for the interactions of cement companies. Simulations show that neither the grandfathering nor the border tax adjustment fully solve the carbon leakage problem because cement companies modify their cement and clinker trade strategies according to the measure applied in order to avoid or reduce their carbon costs. [ABSTRACT FROM AUTHOR]

• An adaptive robust optimization model for a distribution system is proposed. • Capacity expansion decisions comprise charging stations, solar and storage units. • Short-term variability and long-term uncertainty are considered. • Sensitivity analyses are conducted to evaluate changes in investment decisions. The decarbonization of energy systems passes through the transition towards low- and zero-emission vehicles and the investments in efficient technologies. To this end, an adaptive robust optimization approach is proposed for the expansion planning problem of a distribution system where expansion decisions involve the construction of renewable generating units, storage units, and charging stations for electric vehicles. The problem is formulated under the perspective of a central planner that aims at determining the expansion plan that minimizes both investment and operation costs. Both short-term variability and long-term uncertainty are considered in the proposed approach and are modeled in different ways. Short-term variability of the demand, the production of stochastic units, and the price of electricity withdrawn from or injected into the transmission system is modeled using a number of representative days corresponding to different operating conditions. Long-term uncertainty in the future peak demands, the future value of electricity exchanged with the transmission grid, and the number of electric vehicles is instead modeled through confidence bounds. A case study based on a 69-node distribution network shows the effectiveness of the proposed technique and the relationship between the optimal expansions decisions, the revenues from selling electricity to the electric vehicles, the degree of independence from the transmission system, and the role played by the investment budget availability. Moreover, an ex-post decarbonization analysis is conducted to evaluate the environmental impact of the adoption of electric vehicles. Finally, the proposed approach outperforms the results of a stochastic model in terms of computational performance. [ABSTRACT FROM AUTHOR]