{"title":"A Stochastic Methodology to Determine Reinforcement Cost of Power Distribution Grid for Integrating Increasing Share of Renewable Energies and Electric Vehicles","authors":"T. Vu","doi":"10.1109/EEM.2018.8469890","DOIUrl":"https://doi.org/10.1109/EEM.2018.8469890","url":null,"abstract":"Increasing number of installed renewable energies (REs) has revealed many congestions within the distribution grid. In addition, the number of electric vehicles (EV s) are expected to grow rapidly in near future. Both situations increase requirements for reinforcement arrangements. Consequently, the demand for concepts and tools estimating the reinforcement dimensions and costs on regional and national level has arisen. However, challenges in terms of complexity, heterogeneity and data availability of the grid impede the design of the models. This paper presents a stochastic methodology to quantify the costs and a comparison to existing studies. Results indicate that total required lengths for distribution grid line expansion over all voltage levels of the German power grid case and their total cost deviate moderate to renowned methods for REs. Furthermore, increasing share of EV s lead to much higher grid reinforcement requirements than REs and Voltage Regulating Distribution Transformer can reduce REs grid line expansion costs dramatically.","PeriodicalId":334674,"journal":{"name":"2018 15th International Conference on the European Energy Market (EEM)","volume":"347 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115296340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Helseth, Marianne Haugen, S. Jaehnert, B. Mo, H. Farahmand, C. Naversen
{"title":"Multi-Market Price Forecasting in Hydro-Thermal Power Systems","authors":"A. Helseth, Marianne Haugen, S. Jaehnert, B. Mo, H. Farahmand, C. Naversen","doi":"10.1109/EEM.2018.8469932","DOIUrl":"https://doi.org/10.1109/EEM.2018.8469932","url":null,"abstract":"This paper presents a framework for price forecasting in hydro-thermal power systems. The framework consists of a long-term strategic and a short-term operational model. The strategic model provides the end-of-horizon valuation of water in hydro storages as input to the operational model. We emphasize on the operational model, and discuss work in progress to facilitate more detailed fundamental market modeling to enable realistic multi-market price forecasting. A case study of the Nordic power system demonstrates the use of the framework, quantifying the impact of constraints on cable ramping and reserve capacity on prices.","PeriodicalId":334674,"journal":{"name":"2018 15th International Conference on the European Energy Market (EEM)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116170609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The Evolution of the Italian Power System to Support the National Goal of 55% of Renewables on Electricity Consumption","authors":"F. Lanati, M. Gaeta","doi":"10.1109/EEM.2018.8469966","DOIUrl":"https://doi.org/10.1109/EEM.2018.8469966","url":null,"abstract":"With the Decree of 10 November 2017, the new National Energy Strategy (NES2017) was adopted by the Italian government. In the NES2017, electricity production from RES reaches a share, in 2030, equal to 55% of the gross domestic consumption of electricity (GDC-E). In this paper we present the impact assessment of the achievement of such target on the development and on the operation of the power system. The analysis started from an energy scenario developed with the long term energy model TIMES-Italia. Then, using sMTSIM, a simulation model of the Italian power system, the main criticalities (congestion, overgeneration, energy not supplied, etc.) have been identified and quantified. The technical and regulatory measures to mitigate the impact of such critical issues, have been evaluated on the basis of a cost/benefit analysis. Finally, the amount of investments needed and the impact on electricity cost have been estimated.","PeriodicalId":334674,"journal":{"name":"2018 15th International Conference on the European Energy Market (EEM)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116207291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Allegretti, M. Dicorato, G. Forte, M. Trovato, B. Aluisio, C. Gadaleta, C. Vergine
{"title":"Phase Shifting Transformer in Flow-Based Yearly Network Analysis for European Market Envision","authors":"A. Allegretti, M. Dicorato, G. Forte, M. Trovato, B. Aluisio, C. Gadaleta, C. Vergine","doi":"10.1109/EEM.2018.8469801","DOIUrl":"https://doi.org/10.1109/EEM.2018.8469801","url":null,"abstract":"The presence of power flow control devices is assuming increasing importance in international power exchange management. In this paper, the influence of proper Phase Shifting Transformer model on a flow-based procedure for yearly scenario network analysis, starting from zonal market structure, is assessed, in order to inspect the impact on national flowgates. In particular, linear Power Transfer Distribution Factors and shift factors introduced by the regulation of transformer are accounted for network model. The procedure is applied to a provisional model of European power system and high voltage network.","PeriodicalId":334674,"journal":{"name":"2018 15th International Conference on the European Energy Market (EEM)","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127297840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"An Agent-Based Model for Simulating Smart Grid Innovations","authors":"Philippe Schädler, H. Wache, E. Merelli","doi":"10.1109/EEM.2018.8469939","DOIUrl":"https://doi.org/10.1109/EEM.2018.8469939","url":null,"abstract":"In order to derive indicators for the future grid and market stability, in this paper an agent-based model is introduced, to simulate various scenarios. This includes new market designs, market mixes, emerging technological inventions and new regulations. Consumers demand energy based on seasonal variations or changing prices. The suppliers' production might also depend on seasonal variations, on the local solar irradiation or its flexibility; the a bility to react to the market requests. The model introduced in this paper has been used to describe an example scenario of the year 2035, representing a market mix that includes a variety different consumers and suppliers. Eventually it shows, how the model can be applied to model various scenarios and how the resulting grids frequency, the market prices and suppliers profit can be used as indicators for the grid and market stability.","PeriodicalId":334674,"journal":{"name":"2018 15th International Conference on the European Energy Market (EEM)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124829042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Benefits of Clearing Capacity Markets in Short Term Horizon: The Case of Germany","authors":"A. Sadeghi, S. S. Torbaghan, M. Gibescu","doi":"10.1109/EEM.2018.8469979","DOIUrl":"https://doi.org/10.1109/EEM.2018.8469979","url":null,"abstract":"Capacity Remuneration Mechanisms are widely accepted mechanisms to ensure adequate supply capacity for electrical energy systems. Most capacity market designs are focused on long term horizons. We develop an optimization-based framework for clearing the capacity market over a short-term horizon. Participation strategy for generation units with firm capacity based on long-run marginal pricing is analyzed. Based on a German case study, we compare three different market frameworks: a proposed short-term capacity clearing process, a long-term capacity clearing process, and an electricity-only market. Results show that, under perfect competition assumptions, generation units that are necessary to maintain the level of supply adequacy deemed by the TSO, are able to recover their total costs under both short-term and long-term clearing, while the energy-only approach cannot ensure long-term supply adequacy. We also show that clearing the capacity market in short term horizons will decrease the cost of ensuring adequate supply by about 28 %.","PeriodicalId":334674,"journal":{"name":"2018 15th International Conference on the European Energy Market (EEM)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124294687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Improving Electricity Price Forecasting Trough Data Segmentation Based on Artificial Immune Systems","authors":"J. Nuno Fidalgo, Eduardo F. N. R. Da Rocha","doi":"10.1109/EEM.2018.8469954","DOIUrl":"https://doi.org/10.1109/EEM.2018.8469954","url":null,"abstract":"The price evolution in electricity market with large share of renewables often exhibits a deep volatility, triggered by external factors such as wind and water availability, load level and also by business strategies of market agents. Consequently, in many real applications, the performance of electricity price is not appropriate. The goal of this article is to analyze the available market data and characterize circumstances that affect the evolution of prices, in order to allow the identification of states that promote price instability and to confirm that class segmentation allows increasing forecast performance. A regression technique (based on Artificial Neural Networks) was applied first to the whole set and then to each class individually. Performances results showed a clear advantage (above 20%) of the second approach when compared to the first one.","PeriodicalId":334674,"journal":{"name":"2018 15th International Conference on the European Energy Market (EEM)","volume":"154 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122721116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
R. Petrichenko, K. Baltputnis, D. Sobolevsky, A. Sauhats
{"title":"Estimating the Costs of Operating Reserve Provision by Poundage Hydroelectric Power Plants","authors":"R. Petrichenko, K. Baltputnis, D. Sobolevsky, A. Sauhats","doi":"10.1109/EEM.2018.8469876","DOIUrl":"https://doi.org/10.1109/EEM.2018.8469876","url":null,"abstract":"The transmission system operators are entrusted with the task of ensuring stable and reliable functioning of the power system. In a modern power system, there are three groups of market actors who can be called upon by TSOs to aid in power system operation management: controllable generation, energy storage and controllable load. The owners of these assets can rightfully expect remuneration for their services from the TSO to recoup investment costs and operational expenses. This study is focused on poundage hydroelectric power plants and it strives to estimate the cost of operating reserve provision. Data of the large hydroelectric power plants located on the River Daugava is used for the calculation of costs and validation of the proposed algorithms.","PeriodicalId":334674,"journal":{"name":"2018 15th International Conference on the European Energy Market (EEM)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122913165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Probabilistic Forecasting of Electricity Prices Using Kernel Regression","authors":"Grzegorz Dudek","doi":"10.1109/EEM.2018.8469930","DOIUrl":"https://doi.org/10.1109/EEM.2018.8469930","url":null,"abstract":"Electricity price forecasting has become crucial for energy companies due to its fundamental importance for decision making processes and operational management. Electricity price time series exhibit variable means, significant volatility and spikes, which places high demands on forecasting models. Moreover, in recent years researchers and practitioners have come to understand the limitations of point forecasts and require models to generate probabilistic forecasts. In contrast to point forecasts, the probabilistic forecasts takes the form of a predictive probability distribution over future quantities or events of interest. In the paper the probabilistic forecasting model based on Nadaraya- Watson estimator is proposed. The model generates the point forecasts as 24-component vectors representing day-ahead electricity prices. The probabilistic forecasts are calculated as quantiles based on the residual distribution for historical data forecasts. The performance of the proposed model is validated by testing on data from the Polish electricity market.","PeriodicalId":334674,"journal":{"name":"2018 15th International Conference on the European Energy Market (EEM)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131511399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Evaluation of Wind Energy Forecasts: the Undervalued Importance of Data Preparation","authors":"G. Goretti, A. Duffy","doi":"10.1109/EEM.2018.8469845","DOIUrl":"https://doi.org/10.1109/EEM.2018.8469845","url":null,"abstract":"The evaluation of wind energy forecasts is a key task for those involved in the wind power sector, and the accurate evaluation of forecasts is fundamental to make informed decisions both in business and research. To evaluate the accuracy of a forecast, observed values must be compared against forecast values over a test period. At times, however, the actual generation of a wind farm can be affected by factors that are outside the scope of the forecast model. Evaluating a forecast using a data set that includes such out-of-scope observations might give a biased or inconsistent assessment. In the data preparation phase, then, the evaluator should identify out-of-scope data and decide whether to include or remove these from the data set. In this paper, we carry out an empirical study based on data from an existing wind farm and a number of day-ahead forecasts in order to highlight the effects of including in- and out-of-scope data on forecast accuracies. The results show that the outcome of the evaluation varies significantly depending on the criteria adopted in the data selection.","PeriodicalId":334674,"journal":{"name":"2018 15th International Conference on the European Energy Market (EEM)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131627737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}