scholarly journals Future economic perspective and potential revenue of non-subsidized wind turbines in Germany

2021 ◽  
Vol 6 (1) ◽  
pp. 177-190
Author(s):  
Lucas Blickwedel ◽  
Freia Harzendorf ◽  
Ralf Schelenz ◽  
Georg Jacobs
Keyword(s):  
Wind Turbines ◽  
Electricity Markets ◽  
Business Models ◽  
Market Price ◽  
Order Effect ◽  
Electricity Price ◽  
Future Market ◽  
New Business ◽  
Uncertain Future ◽  
Revenue Estimation

Abstract. Fixed feed-in tariffs based on the Renewable Energy Act grant secure revenues from selling electricity for wind turbine operators in Germany. Anyhow, the level of federal financial support is being reduced consecutively. Plant operators must trade self-sufficiently in the future and hence generate revenue by selling electricity directly on electricity markets. Therefore, uncertain future market price developments will influence investment considerations and may lead to stagnation in the expansion of renewable energies. This study estimates future revenue potentials of non-subsidized wind turbines in Germany to reduce this risk. The paper introduces and analyses a forecasting model that generates electricity price time series suited for revenue estimation of wind turbines based on the electricity exchange market. Revenues from the capacity market are neglected. The model is based on openly accessible data and applies a merit-order approach in combination with a simple agent-based approach to forecast long-term day-ahead prices at an hourly resolution. The hourly generation profile of wind turbines can be mapped over several years in conjunction with fluctuations in the electricity price. Levelized revenue of energy is used to assess both dynamic variables (electricity supply and price). The merit-order effect from the expansion of renewables as well as the phasing out of nuclear energy and coal are assessed in a scenario analysis. Based on the assumptions made, the opposing effects could result in a constant average price level for Germany over the next 20 years. The influence of emission prices is considered in a sensitivity analysis and correlates with the share of fossil generation capacities in the generation mix. In a brief case study, it was observed that current average wind turbines are not able to yield financial profit over their lifetime without additional subsidies for the given case. This underlines a need for technical development and new business models like power purchase agreements. The model results can be used for setting and negotiating appropriate terms, such as energy price schedule or penalties for those agreements.

scholarly journals Future Economic Perspective and Potential Revenue of Non-Subsidized Wind Turbines in Germany

2020 ◽  
Author(s):  
Lucas Blickwedel ◽  
Freia Harzendorf ◽  
Ralf Schelenz ◽  
Georg Jacobs
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Electricity Markets ◽  
Nuclear Power ◽  
Power Plants ◽  
Business Models ◽  
Future Market ◽  
New Business ◽  
Development Goals ◽  
Uncertain Future

Abstract. Thanks to the German Renewable Energy Act todays wind turbine operator are dealing with low risk on the revenue side in Germany. Fixed feed-in compensation ensures planning security and high system utilisation. Anyhow, the level of financial support is being reduced consecutively. Therefore, tomorrow’s plant operators have to trade self-sufficiently on European electricity markets hence generate revenue only by marketing electricity. Against the background of uncertain future market developments as well as stagnation in the expansion of renewable energies in Germany, it is of interest to estimate future revenue potentials of those non-subsidized wind turbines. This way investment risks can be reduced and development goals for tomorrow’s wind turbine technology can be deduced. To address this topic, a model has been developed using a modified merit-order approach to forecast long-term day-ahead prices on European electricity markets at an hourly resolution. The model is solely based on open access data. The results show how changes in the German power generation landscape like dismantling of coal and nuclear power plants as well as different emission prices impact the wind turbines potential revenue. A scenario analysis highlights that most of today’s wind turbines are not able to yield financial profit over their lifetime without guaranteed subsidies in Germany. This underlines an urgent need for technical development and new business models. Possible business models could be Power Purchase Agreements (PPA) for which the model results can be used for setting and negotiating appropriate terms, such as an energy price schedule or penalties. Moreover, the results can be used as input for investment calculation and analysis. Hence, the given forecasting model can help to reduce risks on revenue side for plant operators and finally support the expansion of wind energy as a whole.

scholarly journals Neural Network Based Model Comparison for Intraday Electricity Price Forecasting

Energies ◽  
2019 ◽  
Vol 12 (23) ◽  
pp. 4557 ◽  
Author(s):  
Ilkay Oksuz ◽  
Umut Ugurlu
Keyword(s):  
Neural Networks ◽  
Electricity Markets ◽  
Electricity Market ◽  
Network Architecture ◽  
Model Comparison ◽  
Market Price ◽  
Absolute Error ◽  
Electricity Price ◽  
Price Forecasting ◽  
Electricity Price Forecasting

The intraday electricity markets are continuous trade platforms for each hour of the day and have specific characteristics. These markets have shown an increasing number of transactions due to the requirement of close to delivery electricity trade. Recently, intraday electricity price market research has seen a rapid increase in a number of works for price prediction. However, most of these works focus on the features and descriptive statistics of the intraday electricity markets and overlook the comparison of different available models. In this paper, we compare a variety of methods including neural networks to predict intraday electricity market prices in Turkish intraday market. The recurrent neural networks methods outperform the classical methods. Furthermore, gated recurrent unit network architecture achieves the best results with a mean absolute error of 0.978 and a root mean square error of 1.302. Moreover, our results indicate that day-ahead market price of the corresponding hour is a key feature for intraday price forecasting and estimating spread values with day-ahead prices proves to be a more efficient method for prediction.

Improving the Electricity Price Prediction Accuracy by Applying Combined Prediction Models

Vestnik MEI ◽  
2020 ◽  
Vol 6 (6) ◽  
pp. 119-128
Author(s):  
Anna V. Shikhina ◽  
◽  
Tatyana V. Yagodkina ◽  
Keyword(s):  
Electricity Markets ◽  
Prediction Accuracy ◽  
Regression Models ◽  
Prediction Models ◽  
Free Market ◽  
Moving Average ◽  
Market Price ◽  
Electricity Price ◽  
Multifactor Models ◽  
Price Prediction

The solution of problems concerned with predicting a free market price for electricity through constructing different prediction models is considered. In so doing, a shift is made from an analysis of conventional regression and auto-regression models of the moving average to the proposed combined multifactor models, which also include the time trend and dummy variables. This shift is partly justified by the specific behavior of the electricity price in the free market, which is caused by a strictly cyclic change of its value, e.g., proceeding from such attributes as the heating season, day of week, etc. The techniques of constructing combined prediction models has been developed to the level of elaborating effective computational procedures based on the Statistica and OsiSoft PI-System software packages. The application of the autoregressive and combined regression prediction models to the Russian market has demonstrated their fairly good effectiveness with an acceptable level of accuracy. A comparison of the achieved levels of accuracy provided by the competing models has not shown any advantages of the shift to the use of combined regression multifactor models in terms of achieving better prediction accuracy; however, their application for analyzing the influence of different factors on the predicted variable may become a fundamental advantage in selecting the type of prediction model. Despite their being limited to an analysis of the Belgorod region market, the obtained results demonstrate the achieved prediction accuracy that is as least as good, and in the main is even better than the majority of the data presented in the review of the results for European electricity markets. The article substantiates the advisability of studying the combined regression models as a tool for analyzing the influence of individual factors as components influencing the electricity price formation for the predicted period, given that the accuracy level of the combined regression models corresponds to the currently achieved electricity price prediction accuracy levels.

scholarly journals Impact of Wind and Solar Generation on the Italian Zonal Electricity Price

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5858
Author(s):  
Mahmood Hosseini Imani ◽  
Ettore Bompard ◽  
Pietro Colella ◽  
Tao Huang
Keyword(s):  
Regression Model ◽  
Electricity Market ◽  
Multivariate Regression ◽  
Order Effect ◽  
Electricity Price ◽  
Future Market ◽  
Multivariate Regression Model ◽  
Price Demand ◽  
The Impact ◽  
Solar Generation

This paper assesses the impact of increasing wind and solar power generation on zonal market prices in the Italian electricity market from 2015 to 2019, employing a multivariate regression model. A significant aspect to be considered is how the additional wind and solar generation brings changes in the inter-zonal export and import flows. We constructed a zonal dataset consisting of electricity price, demand, wind and solar generation, net input flow, and gas price. In the first and second steps of this study, the impact of additional wind and solar generation that is distributed across zonal borders is calculated separately based on an empirical approach. Then, the Merit Order Effect of the intermittent renewable energy sources is quantified in every six geographical zones of the Italian day-ahead market. The results generated by the multivariate regression model reveal that increasing wind and solar generation decreases the daily zonal electricity price. Therefore, the Merit Order Effect in each zonal market is confirmed. These findings also suggest that the Italian electricity market operator can reduce the National Single Price by accelerating wind and solar generation development. Moreover, these results allow to generate knowledge advantageous for decision-makers and market planners to predict the future market structure.

scholarly journals Estimating the Benefit from Independent Aggregation in the Day-Ahead Market

2021 ◽  
Vol 58 (3) ◽  
pp. 32-46
Author(s):  
K. Baltputnis ◽  
Z. Broka
Keyword(s):  
Demand Response ◽  
Electricity Markets ◽  
Business Models ◽  
Market Price ◽  
Business Case ◽  
Total System ◽  
Wholesale Market ◽  
Power Exchange ◽  
Compensation Mechanisms ◽  
Framework Development

Abstract As the EU countries are working on adapting the Electricity Directive to allow independent aggregation (IA) of demand response (DR) in all the electricity markets, this paper provides an assessment of potential benefits from DR in the day-ahead market, which has proven particularly challenging for the IA regulatory framework development. The model devised in this study uses data of the public wholesale market price curve from the Nord Pool power exchange to simulate market clearing results with introduction of certain amounts of DR that, via independent aggregation, competes alongside generation and is able to shift the supply curve. The simulated new market equilibrium point allows estimating price reduction capability of demand response, the total system-wide benefits, as well as analysing the potential remuneration mechanisms for independent aggregators and implications on their business models. While the results demonstrated a high value from DR during the peak hours, the overall benefits during average price periods were rather low, thus exposing the unpredictability of the revenue stream and questioning the business case for IA in the day-ahead market. The proposed approach can be used for further analysis of different IA compensation mechanisms, considering the system-wide benefits it brings to the wholesale market.

DIGITAL ECONOMY AND GLOBALIZATION: THE IMPORTANCE OF DIGITAL ENTREPRENEURSHIP DEVELOPMENT

2020 ◽  
Vol 6 (3) ◽  
pp. 17-20
Author(s):  
Farxod Tursunov ◽  
Keyword(s):  
Business Models ◽  
Digital Economy ◽  
Management Systems ◽  
Entrepreneurship Development ◽  
New Business ◽  
Definition Of ◽  
Digital Enterprise ◽  
New Business Models ◽  
Digital Entrepreneurship

The article discusses the role of the digital economy in the development of the country, how it becomes the basis of the economy, new business models and management systems. The opinion of scientistsis analyzed, a definition of a digital enterprise is given

Arts, Culture and the City: An Overview

2020 ◽  
Vol 46 (2) ◽  
pp. 10-21
Author(s):  
Charles Landry
Keyword(s):  
Business Models ◽  
Creative Economy ◽  
City Development ◽  
The Arts ◽  
Industrial Buildings ◽  
Social Innovations ◽  
New Business ◽  
Culture Heritage ◽  
Physical Assets ◽  
Social Importance

More people, more organizations, more towns, cities, regions and countries for more reasons have found that over the last 30 years the arts, their broader culture and overall creativity has something in it for them in renewal and revitalization. Over the last decade there have been over a hundred studies of the economic and social importance or impact of the arts, culture, heritage, the recycling of buildings for cultural purposes, creative quarters and the creative economy across the world. Yet there is much more to the arts, culture and creativity in city development. Places in transition urgently need to develop an overall culture of creativity cu ing across all domains within which the arts can be significant. This can be a painful exercise as old certainties crumble and systems, like education, need rethinking. Yet this can unleash new social innovations, new business models and new forms of citizen engagement. Renewal and transformation together are a cultural project involving a shift in mindset and perspective. Creativity is a primary resource as it creates the conditions from which innovations can emerge. Within this the creative economy sectors, especially when aligned to the dramatic digitization dynamic, play a significant role in developing new products and services, generating jobs, anchoring identity and helping expression. Cultural activities and programming and the physical assets of places, their heritage and older industrial buildings are significant elements in the renewal repertoire.

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