András Mezősi

Bio: András Mezősi is an academic researcher from Corvinus University of Budapest. The author has contributed to research in topics: Energy economics & Renewable energy. The author has an hindex of 4, co-authored 14 publications receiving 58 citations.

Cost-efficiency benchmarking of European renewable electricity support schemes

Abstract: This paper assesses the cost efficiency of renewable electricity (RES-E) support schemes in Europe for the 2000–2015 period based on data collected by CEER from national regulators. A cost efficiency indicator (CEI) and Data Envelopment Analysis (DEA) were applied to measure the economic efficiencies of RES-E support schemes in Europe for the two most deployed renewable technology: photovoltaics and wind. The proposed CEI provides a relative and comparable measure of economic efficiency of support, measuring the cleaned (net of whole sale price) unit support value of RES-E production over consumption. The DEA assessment includes more input variables measuring economic performance of EU member states in RES-E support, where the levelised cost of electricity (LCOE) served as input alongside the CEI. After a detailed literature review introduces previous studies on both types of efficiency measures for renewable deployment the results are presented and explained. This study reveals that although the performance of PV support improved rapidly in the time period, wind generated electricity remained more cost efficient. The DEA found wind support schemes in North European countries (Norway, Sweden, Ireland and Denmark) and PV support schemes in Romania, Malta, Cyprus and Italy to be notably cost efficient. Some of these countries are the traditional large-scale producers (like Denmark or Italy) but ‘newcomers’ also performed well due to technology learning resulting in low entry costs. The DEA shows that financing conditions are perhaps as important as resource endowment in determining cost efficiency of support schemes across EU countries. Based on the outcomes we conclude with recommendations to improve the design for more market oriented RES-E support schemes as required by new EU legislation.

A high-resolution geospatial assessment of the rooftop solar photovoltaic potential in the European Union

Abstract: Rooftop solar photovoltaic (PV) systems can make a significant contribution to Europe's energy transition. Realising this potential raises challenges at policy and electricity system planning level. To address this, the authors have developed a geospatially explicit methodology using up-to-date spatial information of the EU building stock to quantify the available rooftop area for PV systems. To do this, it combines satellite-based and statistical data sources with machine learning to provide a reliable assessment of the technical potential for rooftop PV electricity production with a spatial resolution of 100 m across the European Union (EU). It estimates the levelised cost of electricity (LCOE) using country-specific parameters and compares it to the latest household electricity prices. The results show that the EU rooftops could potentially produce 680 TWh of solar electricity annually (representing 24.4% of current electricity consumption), two thirds of which at a cost lower than the current residential tariffs. Country aggregated results illustrate existing barriers for cost-effective rooftop systems in countries with low electricity prices and high investment interest rates, as well as provide indications on how to address these.

Battery cost forecasting: a review of methods and results with an outlook to 2050

Abstract: Rechargeable batteries are a key enabler to achieve the long-term goal to transform into a climate-neutral society. Within this transformation, battery costs are considered a main hurdle for the market-breakthrough of battery-powered products. Encouraged by this, various studies have been published attempting to predict these, providing the reader with a large variance of forecasted cost that results from differences in methods and assumptions. This article creates transparency by identifying 53 studies that provide time- or technology-specific estimates for lithium-ion, solid-state, lithium–sulfur and lithium–air batteries among more than 2000 publications related to the topic. The relevant publications are clustered according to four applied forecasting methods: technological learning, literature-based projections, expert elicitations and bottom-up modeling. Method-specific assumptions are analyzed in-depth and discussed with regard to their results and empirical evidence. Further, 360 extracted data points are consolidated into a pack cost trajectory that reaches a level of about 70 $ (kW h)−1 in 2050, and 12 technology-specific forecast ranges that indicate cost potentials below 90 $ (kW h)−1 for advanced lithium-ion and 70 $ (kW h)−1 for lithium-metal based batteries. Recent studies show confidence in a more stable battery market growth and, across time-specific studies, authors expect continuously declining battery cost regardless of raw material price developments. However, large cost uncertainties are found to exist on technological and chronological levels that will remain a key challenge for researchers and industry in the future.

Internet of Energy (IoE) and High-Renewables Electricity System Market Design

Abstract: The growing importance of the Internet of Energy (IoE) brands the high-renewables electricity system a realistic scenario for the future electricity system market design. In general, the whole gist behind the IoE is developed upon a somewhat broader idea encompassing the so-called “Internet of Things” (IoT), which envisioned a plethora of household appliances, utensils, clothing, smart trackers, smart meters, and vehicles furnished with tiny devices. These devices would record all possible data from all those objects in real time and allow for a two-way exchange of information that makes it possible to optimize their use. IoT employs the Internet Protocol (IP) and the worldwide web (WWW) network for transferring information and data through various types of networks and gateways as well as sensor technologies. This paper presents an outline stemming from the implications of the high-renewables electric system that would employ the Internet of Energy (IoE). In doing so, it focuses on the implications that IoE brings into the high-renewables electricity market inhabited by smart homes, smart meters, electric vehicles, solar panels, and wind turbines, such as the peer-to-peer (P2P) energy exchange between prosumers, optimization of location of charging stations for electric vehicles (EVs), or the information and energy exchange in the smart grids. We show that such issues as compatibility, connection speed, and most notoriously, trust in IoE applications among households and consumers would play a decisive role in the transition to the high-renewables electricity systems of the 21st century. Our findings demonstrate that the decentralized approach to energy system effective control and operation that is offered by IoE is highly likely to become ubiquitous as early as 2030. Since it may be optimal that large-scale rollouts start in the early 2020s, some form of government incentives and funding (e.g. subsidies for installing wind turbines or solar panels or special feed-in-tariffs for buying renewable energy) may be needed for the energy market to make early progress in embracing more renewables and in reducing the costs of later investments. In addition, there might be some other alternative approaches aimed at facilitating this development. We show that the objective is to minimize the overall system cost, which consists of the system investment cost and the system operating cost, subject to CO2 emissions constraints and the operating constraints of generation units, network assets, and novel carbon-free technologies, which is quite cumbersome given the trend in consumption and the planned obsolescence. This can be done through increasing energy efficiency, developing demand side management strategies, and improving matching between supply and demand side, just to name a few possibilities.