The electricity price changes depending on the time of the day in most countries. In Sweden there is a spot price that changes every hour while China uses Time of Use (ToU) tariff. To avoid the most expensive hours this degree project investigates the feasibility of using lithium- ion batteries to shift the load of an industrial air compressor. The Depth of Discharge and the State of Charge (SoC) for the battery are analyzed to find the optimal use of the battery. Through simulations in MATLAB the degradation-curve and State of Charge were analyzed, which was further used for economics analysis. The feasibility of the system is evaluated by using payback time and Net Present Value (NPV). Results show that a battery has a slightly longer lifetime when it is working in a SoC of 50-70%, but a larger SoC is more profitable from the perspective of NPV. For the SoC of 0-100%, the NPV is about ~9683 US$. Compared to Sweden, using batteries to shift load is more profitable in China, which is mainly due to the high electricity prices. For the same air compressor, the payback time is 5 and 15 years for the investment of batteries in China and Sweden respectively.

Instituições consolidadas, capazes de estabelecer estruturas de regras nas relações entre organizações governamentais e atores econômicos, são importantes para a redução dos custos de transação e para a melhora do ambiente competitivo. Ao assegurar maior estabilidade e reduzir incertezas, as instituições tornam-se determinantes para o crescimento econômico. Tendo-se em vista o processo de fortalecimento das instituições, este artigo analisa o comportamento dos preços de energia elétrica no decorrer dos processos de regulação de integração regional do setor. O modelo de painel aborda a comparação dos preços de energia elétrica industrial e residencial entre 1982 e 2009 para 63 países. Apesar do incremento do ambiente institucional, os benefícios gerados não são observáveis por meio de menor preço final de energia elétrica para países pertencentes a blocos regionais. Dado que as instituições regionais variam entre órgãos intergovernamentais e supranacionais, a sensibilidade percebida pelos preços finais trará impactos diferentes nos formuladores de política e, com isso, com reflexos para a evolução do processo de integração. Mecanismos supranacionais, como ocorre na União Europeia, apresentam dinâmicas distintas dos órgãos intergovernamentais da América do Sul, os quais preservam a independência política do poder executivo. Considerando a preocupação destes sobre os resultados domésticos, suas políticas públicas relacionadas aos preços de energia elétrica tendem a reduzir o apoio à integração regional nesse setor. / Solid institutions that could establish structures of rules in the relations between government and economic actors are important to reduce transaction costs and to improve the competitive environment. Considering the process of strengthening institutions, this paper analyzes the behavior of electricity prices during the regulatory processes of regional integration in this sector. We use a Painel Data analysis for 63 countries between 1982 and 2008. Despite improvements in institutional environment, we show that expected lower final prices of electricity were not observed in countries belonging to regional blocs. The process of integration of regional institutions differs among intergovernmental and supranational structures, so policy makers will respond according its own decision-making process to final price variations. Therefore, it reflects the integration process itself. Supranational mechanisms, as in the European Union, generate their own dynamics, which are distinct from intergovernmental bodies in South America, which maintain the political independence of the executive members. Once they are more concerned about domestic outputs, their policies regarding electricity price reduce the likelihood of regional integration improvements.

Instituições consolidadas, capazes de estabelecer estruturas de regras nas relações entre organizações governamentais e atores econômicos, são importantes para a redução dos custos de transação e para a melhora do ambiente competitivo. Ao assegurar maior estabilidade e reduzir incertezas, as instituições tornam-se determinantes para o crescimento econômico. Tendo-se em vista o processo de fortalecimento das instituições, este artigo analisa o comportamento dos preços de energia elétrica no decorrer dos processos de regulação de integração regional do setor. O modelo de painel aborda a comparação dos preços de energia elétrica industrial e residencial entre 1982 e 2009 para 63 países. Apesar do incremento do ambiente institucional, os benefícios gerados não são observáveis por meio de menor preço final de energia elétrica para países pertencentes a blocos regionais. Dado que as instituições regionais variam entre órgãos intergovernamentais e supranacionais, a sensibilidade percebida pelos preços finais trará impactos diferentes nos formuladores de política e, com isso, com reflexos para a evolução do processo de integração. Mecanismos supranacionais, como ocorre na União Europeia, apresentam dinâmicas distintas dos órgãos intergovernamentais da América do Sul, os quais preservam a independência política do poder executivo. Considerando a preocupação destes sobre os resultados domésticos, suas políticas públicas relacionadas aos preços de energia elétrica tendem a reduzir o apoio à integração regional nesse setor. / Solid institutions that could establish structures of rules in the relations between government and economic actors are important to reduce transaction costs and to improve the competitive environment. Considering the process of strengthening institutions, this paper analyzes the behavior of electricity prices during the regulatory processes of regional integration in this sector. We use a Painel Data analysis for 63 countries between 1982 and 2008. Despite improvements in institutional environment, we show that expected lower final prices of electricity were not observed in countries belonging to regional blocs. The process of integration of regional institutions differs among intergovernmental and supranational structures, so policy makers will respond according its own decision-making process to final price variations. Therefore, it reflects the integration process itself. Supranational mechanisms, as in the European Union, generate their own dynamics, which are distinct from intergovernmental bodies in South America, which maintain the political independence of the executive members. Once they are more concerned about domestic outputs, their policies regarding electricity price reduce the likelihood of regional integration improvements.

We examine the relationship between German wind and solar electricity and French spot price volatility. Using hourly data, we find that French imports from Germany driven by German wind and solar electricity sometimes decrease, sometimes increase the volatility of French spot prices. These two opposing effects depend on the shape of the French supply function and on the French demand. We, therefore, estimate different coefficients for imports depending on different demand levels. We acknowledge the endogeneity problem in identifying these effects and employ instrumental variable techniques to circumvent this problem. Our results show the urgent need for further coordination of national energy policies in order to reduce the potential for negative spill over effects of nationally driven energy policies in neighbouring countries as European electricity markets are becoming more integrated. / Series: Department of Economics Working Paper Series

Within forecasting electricity pricing, we analyse whether adding various vari- ables improves the predictions, and if shorter time intervals between observa- tions enhance accuracy of the forecasting. Next, we focus on proper selection of lagged observations, which has not been thoroughly covered in the past litera- ture. In addition, many papers studied electricity prices in larger markets (e.g. United States, Australia, Nord Pool, etc.) on datasets limited in scope, with 2-3 years timespan. To address these gaps in literature, we obtain one daily and one hourly dataset, both spanning 6 years (January 1, 2015 - December 31, 2020), from four Central and Eastern European countries - the Czech Repub- lic, the Slovak Republic, Hungary, and Romania. These contain information on the electricity prices, and information on our observed added variables - temperature and cross-border electricity flows. For the forecasting, we use two different methods - Autoregression (AR) and Seemingly Unrelated Regression (SUR). The thorough selection of lagged observations, which we accustom to the closing time of the auction-based electricity market system, serves further studies as a guidance on how to avoid possible errors and inconsistencies in their predictions. In our analyses, both AR and SUR models show that...

When the big data time comes, people also need to keep pace with the times to seek and develop tools that can deal with the vast amount of information. In this project, lassois applied to build parametric models of electricity prices based on different affecting factors. Thereafter, the models are used to predict the electricity prices 8 days forward for three different time periods. We compare their prediction performances in terms of normalized mean square error (NMSE) and identify dominant factors of the electricity prices in different time periods using lasso. The results show that a model that spans over a 24 hourlong period gives the lowest NMSE, followed by one spanning over a two hour long period where the electricity prices are leading up to a peak value. The model that obtains the highestNMSE is from a two hour long period, where the electricity prices have a peak value. Besides, we also analyze potential reasons for the results. / När big data-tiden kommer måste även människor hålla jämna steg med tiderna för att söka och utveckla verktyg som kan hantera den stora mängden information. I detta projekt används lasso för att bygga parametriska modeller av elpriser baserade på olika påverkansfaktorer. Därefter används modellerna för att förutsäga elpriserna 8 dagar framåt för tre olika tidsperioder. Vi jämför deras prediktionsprestanda i termer av normaliserat medelkvadratfel (NMSE) och identifierar dominerande faktorer för elpriserna under olika tidsperioder med hjälp av lasso. Resultaten visar att en modell som sträcker sig över en 24 timmar lång period ger lägst NMSE värde, följt av en som sträcker sig över en två timmar lång period där elpriserna leder fram till ett toppvärde. Modellen som får högst NMSE är från en två timmar lång period, där elpriserna har ett toppvärde. Dessutom analyserar vi också potentiella orsaker till resultaten. / Kandidatexjobb i elektroteknik 2022, KTH, Stockholm

Many methods have been used to forecast real-time electricity prices in various regions around the world. The problem is difficult because of market volatility affected by a wide range of exogenous variables from weather to natural gas prices, and accurate price forecasting could help both suppliers and consumers plan effective business strategies. Statistical analysis with autoregressive moving average methods and computational intelligence approaches using artificial neural networks dominate the landscape. With the rise in popularity of convolutional neural networks to handle problems with large numbers of inputs, and convolutional neural networks conspicuously lacking from current literature in this field, convolutional neural networks are used for this time series forecasting problem and show some promising results. This document fulfills both MSEE Master's Thesis and BSCPE Senior Project requirements.

<p>Denna uppsats undersöker vilka faktorer som påverkar förändringar i elspotpriset på Nord Pool. Avsikten är att resultatet skall ligga till grund för en prisuppskattningsmodell för Lunds Energikoncernen AB. Faktorerna bestämdes genom en förstudie där viktig litteratur om elmarknaden studerades samt samtal med Lunds Energikoncernen AB. De faktorer som undersöks i denna uppsats är priset på utsläppsrätter, nettoexport till Tysk-land, temperatur, nederbörd, priset på kol och villaolja samt konjunkturutveckling i Sve-rige.</p><p>Undersökningen av faktorerna bestod av en multipel regressionsanalys med undersökta faktorer som oberoende variabler och elspotpriset på Nord Pool som den beroende va-riabeln. Faktorerna blev indelade i två grupper dagsgruppen och månadsgruppen, grunden till uppdelningen är som namnen antyder att statistiken var observerad dygnsvis och må-nadsvis. I månadsgruppen ingick nettoexport till Tyskland, priset på kol, villaolja samt konjunktur och ur denna grupp visade sig endast nettoexport till Tyskland ha statistisk signifikans.</p><p>I dagsgruppen ingick de faktorer som oftast omnämns i litteraturen som prispåverkande, nämligen temperatur, nederbörd och priset på utsläppsrätter. Dock visade sig nederbörd inte ha någon statistisk signifikant påverkan på elpriset varvid ett nytt test på ett nytt sta-tistiskt underlag gjordes för nederbörden vilket gav samma resultat, vilket var förvånan-de. Både temperatur och priset på utsläppsrätter visade sig dock ha statistisk signifikans och detta intygades genom ytterligare test.</p><p>Härefter gjordes en regressionsanalys med de faktorer visat sig ha statistisk signifikans som oberoende variabler, det vill säga nettoexport till Tyskland, temperatur och priset på utsläppsrätter gentemot elpriset som beroende variabel. Denna enkla prognosmodell kunde förklara så mycket som 70 procent av förändringarna i elpriset.</p><p>Slutligen diskuteras prognosmodellen av författarna, en brist är bland annat att den inte kan förutse hastiga förändringar i priset och att den behöver kalibreras om när den nya handelsperioden för utsläppsrätter sätter i gång 2008. Dock gav analysen positiva signa-ler om att det kan vara möjligt att basera en prismodell på el med de faktorer som har störst inverkan på den dyraste produktionsteknologi som oftast används i elproduktio-nen, då elmarknaden i praktiken tillämpar marginalprissättning.</p> / <p>This thesis examines which factors that drive changes in the electricity spot price on the Nordic energy exchange Nord Pool. The intention with this thesis is to support Lunds Energikoncernen AB to create a pricing model. The factors were determined though a pre-study in which important literature on the electricity market were studied and inter-views with Lunds Energikoncernen AB. The examined factors in this thesis are; the price of emission allowances, net export to Germany, temperature, precipitation, the prices of coal and burning oil and Sweden’s business cycle.</p><p>The factor study was a multiple regression analysis with the above factors as independ-ent variables and the spot price of electricity on Nord Pool as the dependent. The fac-tors were divided in two groups, the day group and the month group, the two groups were decided due to statistical observations. The factors from the former group had daily ob-served data and the latter monthly data. The month group included net exchange with Germany, oil and coal prices and the business cycle which are measured in GDP. In the month group only the net exchange with Germany had statistical significance and was used in further studies.</p><p>In the day group the factors that are mostly discussed in the literature to impact on the electricity price namely, temperature, precipitation, and the price of emission allowances. As it, some what unexpected, turned out the precipitation did not have a statistical affect on the electricity price. The authors chose to carry out another analysis with precipita-tion from another area, neither this result had statistical significance. However, both the temperature and the price of emission allowances did have a statistical significant effect on the electricity price, the result were verified through one more round of analysis.</p><p>After the two initial analyses, a regression analysis with the three factors that had statis-tical significance and the electricity price were used in a final analysis. The factors in-cluded in this regression were net exchange with Germany, temperature and the price of emission allowances. This, somewhat, simple forecasting model explained as much as 70 percent of the changes in the electricity spot price.</p><p>At last the forecasting model were discussed by the authors who identified two major weaknesses, first the model may not explain sudden changes in the electricity price, and second the model has to be re-calibrated when the next trading period for emission al-lowances starts in early 2008. However the analysis did indicate that it might be possible to base an electricity price forecasting model on the factors that affects the most expen-sive production facility that are used to create energy, since the electricity market prac-tice marginal pricing.</p>

Denna uppsats undersöker vilka faktorer som påverkar förändringar i elspotpriset på Nord Pool. Avsikten är att resultatet skall ligga till grund för en prisuppskattningsmodell för Lunds Energikoncernen AB. Faktorerna bestämdes genom en förstudie där viktig litteratur om elmarknaden studerades samt samtal med Lunds Energikoncernen AB. De faktorer som undersöks i denna uppsats är priset på utsläppsrätter, nettoexport till Tysk-land, temperatur, nederbörd, priset på kol och villaolja samt konjunkturutveckling i Sve-rige. Undersökningen av faktorerna bestod av en multipel regressionsanalys med undersökta faktorer som oberoende variabler och elspotpriset på Nord Pool som den beroende va-riabeln. Faktorerna blev indelade i två grupper dagsgruppen och månadsgruppen, grunden till uppdelningen är som namnen antyder att statistiken var observerad dygnsvis och må-nadsvis. I månadsgruppen ingick nettoexport till Tyskland, priset på kol, villaolja samt konjunktur och ur denna grupp visade sig endast nettoexport till Tyskland ha statistisk signifikans. I dagsgruppen ingick de faktorer som oftast omnämns i litteraturen som prispåverkande, nämligen temperatur, nederbörd och priset på utsläppsrätter. Dock visade sig nederbörd inte ha någon statistisk signifikant påverkan på elpriset varvid ett nytt test på ett nytt sta-tistiskt underlag gjordes för nederbörden vilket gav samma resultat, vilket var förvånan-de. Både temperatur och priset på utsläppsrätter visade sig dock ha statistisk signifikans och detta intygades genom ytterligare test. Härefter gjordes en regressionsanalys med de faktorer visat sig ha statistisk signifikans som oberoende variabler, det vill säga nettoexport till Tyskland, temperatur och priset på utsläppsrätter gentemot elpriset som beroende variabel. Denna enkla prognosmodell kunde förklara så mycket som 70 procent av förändringarna i elpriset. Slutligen diskuteras prognosmodellen av författarna, en brist är bland annat att den inte kan förutse hastiga förändringar i priset och att den behöver kalibreras om när den nya handelsperioden för utsläppsrätter sätter i gång 2008. Dock gav analysen positiva signa-ler om att det kan vara möjligt att basera en prismodell på el med de faktorer som har störst inverkan på den dyraste produktionsteknologi som oftast används i elproduktio-nen, då elmarknaden i praktiken tillämpar marginalprissättning. / This thesis examines which factors that drive changes in the electricity spot price on the Nordic energy exchange Nord Pool. The intention with this thesis is to support Lunds Energikoncernen AB to create a pricing model. The factors were determined though a pre-study in which important literature on the electricity market were studied and inter-views with Lunds Energikoncernen AB. The examined factors in this thesis are; the price of emission allowances, net export to Germany, temperature, precipitation, the prices of coal and burning oil and Sweden’s business cycle. The factor study was a multiple regression analysis with the above factors as independ-ent variables and the spot price of electricity on Nord Pool as the dependent. The fac-tors were divided in two groups, the day group and the month group, the two groups were decided due to statistical observations. The factors from the former group had daily ob-served data and the latter monthly data. The month group included net exchange with Germany, oil and coal prices and the business cycle which are measured in GDP. In the month group only the net exchange with Germany had statistical significance and was used in further studies. In the day group the factors that are mostly discussed in the literature to impact on the electricity price namely, temperature, precipitation, and the price of emission allowances. As it, some what unexpected, turned out the precipitation did not have a statistical affect on the electricity price. The authors chose to carry out another analysis with precipita-tion from another area, neither this result had statistical significance. However, both the temperature and the price of emission allowances did have a statistical significant effect on the electricity price, the result were verified through one more round of analysis. After the two initial analyses, a regression analysis with the three factors that had statis-tical significance and the electricity price were used in a final analysis. The factors in-cluded in this regression were net exchange with Germany, temperature and the price of emission allowances. This, somewhat, simple forecasting model explained as much as 70 percent of the changes in the electricity spot price. At last the forecasting model were discussed by the authors who identified two major weaknesses, first the model may not explain sudden changes in the electricity price, and second the model has to be re-calibrated when the next trading period for emission al-lowances starts in early 2008. However the analysis did indicate that it might be possible to base an electricity price forecasting model on the factors that affects the most expen-sive production facility that are used to create energy, since the electricity market prac-tice marginal pricing.

Power systems all over the world advance towards the direction of deregulation in the past few years. Introducing competition mechanism and the principle of market rules in deregulation. Utility companies will face unprecedented changes and challenges. Taiwan power company is also working on the deregulation direction with a competitive environment opened up, it will improve the scientific and technological levels and the service quality of electricity. Load management functions as the marginal price of electricity is predicted. Consumers can get Real-Time Pricing information determine their own buying strategy. One most representative deregulation example in U.S.A. is the PJM(Pennsylvania¡BNew Jersey¡BMaryland)system combining generating, transmitting, distribution and sales of electricity. It offers the information of real-time power supply and is one of the cases in the world. Historical data in the thesis comes from PJM. Artificial Neural Network was designed to the Locational Marginal Price(LMP), considering the factors such as temperature and other relevant data from deregulation with the introduction of various parameters in forecasting, and the use of week as a counting base. LMP will be forecasted. The forecasted results will be to check the accuracy and performance with initial data.