1. Introduction

The electricity markets have been restructured to achieve efficiency and competitiveness. In the timespan 1990-2008, several countries made a liberalization process to have a competitive, transparent, and liable market ^1,2. These structural changes determined a trade that, in contrast to financial markets, is characterized by power storage limits, the low correlation between short-term and long-term prices, and a seasonal dynamic complex ³.

On the other hand, the competitiveness introduction in the generation sector originated a spot market, wherein the electricity price is defined through an auction between suppliers to meet the hourly demand of the next day. Also, the difficulty in storing electricity causes the spot price to present characteristics such as high frequency, mean and variance are not constant, seasonal dynamics, high variability, and sensitivity to demand, besides changes in the generation sources ^3-7. Therefore, modeling and forecasting electricity price is defiance for academics and electricity market agents.

For a better price dynamic understanding, it is necessary to observe the fundamentals or the factors that explain its characteristics in the deregulated electricity markets. Therefore, a bibliometric analysis is proposed as a systematic tool for monitoring the research efforts on-field, allowing an overview of the electricity price determinants in the timespan January 1979 - April 2021. In general, variables that determine electricity prices in competitive markets were observed, such as economic conditions, weather and temperature, power system operations, market structures, agent strategies, and historical factors.

This document is divided into five sections after the introduction: in section 2, the methodology for bibliometric analysis is defined. In section 3, the results are described; in section 4, the determinants of the electricity price in competitive markets are presented through bibliometric analysis results and the case of the Latin American electricity markets is included. In section 5, the summary and conclusions are presented.

2. Methodology

The research was made with the Scopus database and included: (i) citation information, (ii) bibliographical information, (iii) abstract and keyboard, and (iv) other information. Further, the search equation (Eq. 1) includes the terms in the title of the article, abstract, and keywords and was limit to articles. The search equation is described below:

The software VOSviewer 1.6.14 was used for data analysis and visualization ⁸.

3. Results

636 documents published in the study period were observed. Figure 1 shows the number of articles per year. An increase can be noticed since 2003 due to the deregulation and liberalization of the electricity markets during 1990-2008. These structural changes introduced competition, transparency, and efficiency in power markets ^1,9. Therefore, the spot electricity price or market clearing price is defined as the intersection of supply and demand curves by aggregated bids for a particular hour. That is because the spot electricity contracts are auctioned the day before and once per day. Besides, these contracts are hourly agreements with physical delivery ⁵.

However, the spot price structure caused a set of questions about its dynamics and volatility. Therefore, it was observed that the average of documents per year. Before 2003, was one document, but between 2003-2020, the average per year was above 32 manuscripts. Likewise, 22 manuscripts were identified until April 2021.

In the following subsections, the results are described according to the countries and institutions related to modeling the spot price dynamics and its determinants; the subject areas and methodology approach applied to analysis and forecasting of spot prices; and the leading documents and authors focusing on this topic.

3.1. Leading countries and institutions related to the electricity spot price study

Table 1 shows the number of documents by country. The top-five of leading countries that have studied the electricity price dynamics are China with 122 manuscripts, the United States with 98, Germany with 69, the United Kingdom with 57, and Spain with 46. Besides, it was considered ranking the research impact through the number of citations, and the ratio between the number of citations and the number of publications (CP ratio). The United States and the United Kingdom were always within the top five. Despite China was in second place in citations, this country occupied the last place in the CP ratio, where The United States, Canada, and Iran were the most important countries.

Furthermore, Figure 2 shows the countries’ collaboration network for studies on electricity price topics (countries contributing a minimum number of five documents and citations), and seven clusters were observed: red, green, blue, yellow, purple, grey, and orange. The United States, Germany, China, the United Kingdom, Spain, Australia, and Norway have the strongest collaboration network. In Latin America, Brazil and Colombia are the countries with more documents and citations. The first one has eight documents and 70 citations, and the second one has six documents and 51 citations, but Colombia presents a high CP ratio. Likewise, it was found that Mexico has three manuscripts and 30 citations, followed by Uruguay, Chile, Argentina, and Guatemala with one document per country.

Otherwise, Figure 3a shows the number of documents by affiliation, and Figure 3b provides the number of manuscripts by funding sponsor. Firstly, the leading universities in this research area are North China Electric Power University with 18 documents, Norges Teknisk-Naturvitenskapelige Universitet with ten manuscripts, Zhejiang University, and London Business School with eight manuscripts, respectively. Secondly, the most relevant sponsors in this topic are the National Natural Science Foundation of China, the European Commission, the Ministry of Education of People's Republic of China, and the UK Research and Innovation. The top-10 of affiliations and funding sponsors were involved in 15% and 16% of all publications, respectively. Besides, a close relation between countries, universities, and funding sponsors was observed through this analysis.

Figure 4 describes the number of documents in percentage by subject area. The most relevant knowledge areas in this topic are Energy (30.9%), Engineering (18.5%), Environmental Science (14.6%), and Economics, Econometrics, and Finance (14.4%).

3.2. Subject areas and methodology approaches

Given the deregulated process in energy markets, the modeling and forecasting of electricity prices require different methods. Therefore, several engineering techniques are applied to explain price dynamics such as simulation algorithms and artificial intelligence models. By contrast, econometric and statistical approaches as autoregressive or regression models are other methodologies used. According to ¹⁰, techniques used in the literature can be classified into three groups: simulation models, game theory models, and times series analysis, as described below in Table 2. The time series models present more extensive applicability through stochastic models, causal models, or artificial intelligence models such as neural networks, machine learning, or data-mining models.

3.3. Leading authors and documents

Table 3 shows the most relevant authors with their affiliation and the number of documents in the period of study. The top five leading authors of electricity price fundamentals are Zarnikau with six documents and Woo, Reneses, Bunn, and Benth with five manuscripts each of them. These findings are coincident with the information reported in Table 1 and Figure 2, i.e., the most productive authors and the countries where their institutions are located.

By contrast, the authors’ keywords, electricity markets, electricity prices, electricity price forecasting, renewable energy, and electricity, had 127, 126, 44, 37, and 36 occurrences, respectively. The top 10 author keywords with their number of occurrences are shown in Table 4. Likewise, Figure 5 shows the research topic network visualization of publications related to electricity price fundamentals, and six clusters were observed (the minimum number of occurrences of a keyword is five). The primary node is the electricity market and has links with electricity prices, empirical methods (quantile regression, neural network, or forecasting), uncertainty, risk, and market characteristics (deregulation, electricity generation, energy efficiency, energy policy, or renewable energy).

Furthermore, Table 5 shows the most cited research related to electricity price fundamentals based on Scopus reports. The most cited paper analyzed the market liberalization effects on the elasticity of the demand for electricity ⁴². After electricity market liberalization, the consumers are more exposed to the electricity prices volatility and may decide to modify their demand profile to reduce their costs. Therefore, the authors considered a pool electricity market with scheduling generation and setting the electricity prices, using 26 generator systems. The results showed that the elasticity of the demand is an important factor to be considered when the price is setting in a centralized competitive market. In the next section, the different fundamentals of electricity prices found in the bibliometric analysis are discussed.

4. Determinants of electricity price in deregulated markets: Discussion

After the liberalization of electricity markets, the price has shown high volatility, mean-reversion, seasonality, extreme spikes, and its dynamic is explained by a set of fundamentals ⁶. According to ^5,10, the most important factors to explain the electricity price dynamics were categorized into five groups: i) market characteristics, ii) fundamental factors, iii) operational factors, iv) strategic factors, and v) historical factors. Table 6 summarizes the principal determinants of electricity spot prices observed in the literature.

In the short term and as of the electricity spot price characteristics, its high variability is related to stational patterns transmit by variables such as hourly demand, load conditions, and generating capacity for a particular day. Besides, the electricity storage limitations cause relevant changes in price distribution by external event shocks as weather fluctuation, production and transmission failures, or shortage in generation sources (e.g., water, gas, or coal).

Likewise, electricity demand captures consumption changes of different economic sectors as industrial, commercial, and residential. Therefore, the dynamic of these activities modifies the price structures due to the demand is inelastic in the short term.

Other factors related to increasing price variances are the market structure, energy policy, agent strategies, and the impossibility of achieving the demand and supply equilibrium in real-time. Therefore, modeling the electricity spot price dynamic represents a challenge to market agents. In the last years, the investigation trend about modeling the electricity spot price has shown a significant effort to applied different forecasting methodologies. Since the introduction of the competitive electricity markets, spot price forecasting has become a relevant decision-making mechanism by many companies.

According to ^68,69, the inception of smart grids and renewable integration has increased the uncertainty on some market indicators as the spot price, demand, and supply. For example, renewable energies can reduce price volatility in Hydrothermal power markets ⁷⁰. Thus, the bibliometric analysis showed that approximately 60% of the papers of the last five years had the price forecast as the main objective ^29,66,71-74. In this way, price prediction has become a principal method for planning and operations in energy systems. Besides, the growing popularity of artificial intelligence methods, characterized by fast fitting and low memory usage, allow simple price forecasting ^{21-23,25,75-78}.

4.1. Latin America case

Figure 6 shows the electricity generation by fuel in 2019. There is a high dependence on natural gas and coal for power generation in North America (57%), the Commonwealth of Independent States (CIS) (67%), the Middle East (64%), the Asian Pacific (70%), and Africa (68%). By contrast, Europe has a balanced energy matrix where each source weight, except oil, is between 16%-23%. However, South and Central America gets more than half of its power from hydroelectricity, with a share far higher than any other region. Besides, fuel fossil has a total percentage of 31%. Therefore, in S. & C. America, the generation is based on hydroelectric and thermal power sources.

Despite countries such as Paraguay, Uruguay, and Costa Rica, the market is integrated and depends on the state, liberalization process in Latin America showed that energy policy, fuel prices, and hydrological conditions are the fundamentals that explain electricity price fluctuations ^80,81. In Central America, ³¹ described that electricity price fluctuations are related to hydrology changes and fossil fuel price variations in Guatemala, El Salvador, and Panama. Similarly, in Mexico, the prices are correlated with fuels because close to 46% of the energy market depends on fossil sources ⁸². Also, the price increase depends on growing congestion in the national electricity transmission network ⁸³.

In Chile, the empirical evidence shows that price dynamics depend on fuel prices and hydrology ^84-86. Besides, ⁸⁷ observed that spot prices are related to uncertainty and investment decisions making. Thus, the fundamentals of prices in the long term were the structure of the transmission system, the hydrology, and contract prices. Meanwhile, in Brazil, the spot price variations are linked to historical factors, hydrological conditions, generation sources, and energy demand ^88-91. However, ⁹² described that electricity prices were skyrocketing during 2011-2015 not only to unfavorable hydrology but also to problems in the planning and execution of the expansion of power generation and transmission, and difficulties with the NEWAVE dispatch software used by the government.

Colombia has many studies about price dynamics since its energy market is based on hydrothermal power generation. Hydropower represents close to 70% of energy production and the rest depends on thermal power for periods of high demand or temperature shocks ⁷⁰. The studies showed that variables such as electricity demand and supply, reservoir levels, weather, fuel prices, power transmission failures, energy policy, bilateral contracts, and agent strategies are fundamentals to determine spot price structure ^4,93-97.

5. Conclusions

A total of 636 articles related to electricity price fundamentals were published in timespan January 1979 - April 2021. A significant increase in the number of documents after 2000 was observed due to electricity market liberalizations during 1990-2008. Furthermore, countries such as the United States and the United Kingdom are the research leaders. However, the United States, Canada, and Iran had a high impact on their CP ratio. Also, the main investigation areas related to the fundamentals of electricity prices were Energy, Engineering, Environmental Science, and Economics, Econometrics, and Finance.

On the other hand, the most cited papers showed that the factors are categorized into five groups: i) market characteristics, ii) fundamental factors, iii) operational factors, iv) strategic factors, and v) historical factors. However, and independent of market structure, price volatility always is given by factors such as economic conditions, weather, operation of power systems, and customer demands.

Besides, the investigation trend observed in this study is related to forecasting the electricity spot price since the inception of competitive power markets. The academics and market agents have understood that electricity spot price and load forecasting are relevant for energy systems and operations than ever before. Therefore, the price fundamentals give a new perspective to understand the price dynamic. Likewise, the growing renewable sources and the inception of smart grids reduce the gap between the technical business and financial business, consequently the uncertainty increase.

Finally, the future research proposal can be divided into two topics: first, apply advanced empirical methods of artificial intelligence and time series analysis to evaluate the fundamentals of the electricity price on these markets. Second, to identify the electricity prices effects on different sectors such as industry or economic activity.

6. Acknowledgements and/or funding statement

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors

7. References

Weron R. Modeling and forecasting electricity loads and prices: a statistical approach. Chichester, England; Hoboken, NJ: John Wiley & Sons; 2006. 178 p. (Wiley finance series).

Weron R, Misiorek A. Forecasting spot electricity prices: A comparison of parametric and semiparametric time series models. Int J Forecast. 2008;24(4):744-63. Doi: 10.1016/j.ijforecast.2008.08.004

Gil-Zapata MM, Maya-Ochoa C. Modelación de la volatilidad de los precios de la energía eléctrica en Colombia. Rev Ing Univ Medellín. 2008;(12):87-114.

Castaño E, Sierra J. Sobre la existencia de una raíz unitaria en la serie de tiempo mensual del precio de la electricidad en Colombia. Lect Econ. 2012;259-91.

Girish GP, Vijayalakshmi S. Determinants of Electricity Price in Competitive Power Market. Int J Bus Manag. 2013;8(21):70-5. Doi: 10.5539/ijbm.v8n21p70

Huisman R, Mahieu R. Regime jumps in electricity prices. Energy Econ. 2003;25(5):425-434. Doi: 10.1016/S0140-9883(03)00041-0

Yun Z, Quan Z, Caixin S, Shaolan L, Yuming L, Yang S. RBF neural network and ANFIS-based short-term load forecasting approach in real-time price environment. IEEE Trans Power Syst. 2008;23(3):853-8. Doi: 10.1109/TPWRS.2008.922249

van Eck NJ, Waltman L. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics. 2010;84(2):523-38. Doi: 10.1007/s11192-009-0146-3

Longstaff FA, Wang AW. Electricity Forward Prices: A High-Frequency Empirical Analysis. J Finance. 2004;59(4):1877-1900.

Aggarwal SK, Saini LM, Kumar A. Electricity price forecasting in deregulated markets: A review and evaluation. Int J Electr Power Energy Syst. 2009;31(1):13-22. Doi: 10.1016/j.ijepes.2008.09.003

Xie L, Zheng H, Zhang L. Electricity price sensitivity analysis based on numerical simulation. Dianli Xitong Zidonghua Automation Electr Power Syst. 2009;33(12):38-42.

Felten B. An integrated model of coupled heat and power sectors for large-scale energy system analyses. Appl Energy. 2020;266:114521. Doi: 10.1016/j.apenergy.2020.114521

Askari MT, Ab Kadir MZA, Hizam H, Jasni J. A new comprehensive model to simulate the restructured power market for seasonal price signals by considering on the wind resources. J Renew Sustain Energy. 2014;6(2):023104. Doi: 10.1063/1.4869141

Motamedi Sedeh O, Ostadi B. Optimization of bidding strategy in the day-ahead market by consideration of seasonality trend of the market spot price. Energy Policy. 2020;145:111740. Doi: 10.1016/j.enpol.2020.111740

Krecar N, Gubina AF. Risk mitigation in the electricity market driven by new renewable energy sources. Wiley Interdiscip Rev Energy Environ. 2020;9(1):e362. Doi: 10.1002/wene.362

Joskow PL, Kahn E. A quantitative analysis of pricing behavior in California's wholesale electricity market during summer 2000. Energy J. 2002;23(4):1-35. Doi: 10.5547/ISSN0195-6574-EJ-Vol23-No4-1

Brouwer AS, van den Broek M, Zappa W, Turkenburg WC, Faaij A. Least-cost options for integrating intermittent renewables in low-carbon power systems. Appl Energy. 2016;161:48-74. Doi: 10.1016/j.apenergy.2015.09.090

Jurasz J, Kies A, Zajac P. Synergetic operation of photovoltaic and hydro power stations on a day-ahead energy market. Energy. 2020;212:118686. Doi: 10.1016/j.energy.2020.118686

Hellwig M, Schober D, Woll O. Measuring market integration and estimating policy impacts on the Swiss electricity market. Energy Econ. 2020;86:104637. Doi: 10.1016/j.eneco.2019.104637

Keles D, Scelle J, Paraschiv F, Fichtner W. Extended forecast methods for day-ahead electricity spot prices applying artificial neural networks. Appl Energy. 2016;162:218-30. Doi: 10.1016/j.apenergy.2015.09.087

Rafiei M, Niknam T, Khooban M-H. Probabilistic Forecasting of Hourly Electricity Price by Generalization of ELM for Usage in Improved Wavelet Neural Network. IEEE Trans Ind Inform. 2017;13(1):71-9. Doi: 10.1109/TII.2016.2585378

Bento PMR, Pombo JAN, Calado MRA, Mariano SJPS. A bat optimized neural network and wavelet transform approach for short-term price forecasting. Appl Energy. 2018;210:88-97. Doi: 10.1016/j.apenergy.2017.10.058

Mujeeb S, Javaid N, Ilahi M, Wadud Z, Ishmanov F, Afzal MK. Deep long short-term memory: A new price and load forecasting scheme for big data in smart cities. Sustain. 2019;11(4):987. Doi: 10.3390/su11040987

Kath C. Modeling intraday markets under the new advances of the cross-border Intraday Project (XBID): Evidence from the German intraday market. Energies. 2019;12(22):4339. Doi: 10.3390/en12224339

Demir S, Mincev K, Kok K, Paterakis NG. Introducing technical indicators to electricity price forecasting: A feature engineering study for linear, ensemble, and deep machine learning models. Appl Sci. 2020;10(1):255. Doi: 10.3390/app10010255

Karakatsani NV, Bunn DW. Intra-day and regime-switching dynamics in electricity price formation. Energy Econ. 2008;30(4):1776-97. Doi: 10.1016/j.eneco.2008.02.004

Forbes KF, Zampelli EM. Do day-ahead electricity prices reflect economic fundamentals? Evidence from the California ISO. Energy J. 2014;35(3):129-44. Doi: 10.5547/01956574.35.3.6

Tranberg B, Hansen RT, Catania L. Managing volumetric risk of long-term power purchase agreements. Energy Econ. 2020;85:104567. Doi: 10.1016/j.eneco.2019.104567

Hinderks WJ, Wagner A. Factor models in the German electricity market: Stylized facts, seasonality, and calibration. Energy Econ. 2020;85:104351. Doi: 10.1016/j.eneco.2019.03.024

Mosquera-López S, Uribe JM, Manotas-Duque DF. Nonlinear empirical pricing in electricity markets using fundamental weather factors. Energy. 2017;139:594-605. Doi: 10.1016/j.energy.2017.07.181

Blazsek S, Hernández H. Analysis of electricity prices for Central American countries using dynamic conditional score models. Empir Econ. 2018;55(4):1807-48. Doi: 10.1007/s00181-017-1341-3

Mosquera-López S, Nursimulu A. Drivers of electricity price dynamics: Comparative analysis of spot and futures markets. Energy Policy. 2019;126:76-87. Doi: 10.1016/j.enpol.2018.11.020

Yan D, Kong Y, Ren X, Shi Y, Chiang S. The determinants of urban sustainability in Chinese resource-based cities: A panel quantile regression approach. Sci Total Environ. 2019;686:1210-9. Doi: 10.1016/j.scitotenv.2019.05.386

Díaz G, Coto J, Gómez-Aleixandre J. Prediction and explanation of the formation of the Spanish day-ahead electricity price through machine learning regression. Appl Energy. 2019;239:610-25. Doi: 10.1016/j.apenergy.2019.01.213

Zhao W, Yan H, He W. Equilibrium Model of Electricity Market Based on Non-Cooperative Game of Wind Farms, Thermal Power Plants and Power Grid Company. Dianwang JishuPower Syst Technol. 2018;42(1):103-9. Doi: 10.13335/j.1000-3673.pst.2017.1337

Pakrooh P, Nematian J, Pishbahar E, Hayati B. Reforming energy prices to achieve sustainable energy consumption in the agriculture sector of Iran's provinces: Using game approach. J Clean Prod. 2021;293:126146. Doi: 10.1016/j.jclepro.2021.126146

Lu Q, Lü S, Leng Y. A Nash-Stackelberg game approach in regional energy market considering users' integrated demand response. Energy. 2019;175:456-70. Doi: 10.1016/j.energy.2019.03.079

Penkovskii AV, Stennikov VA, Khamisov OV. Optimum load distribution between heat sources based on the Cournot model. Therm Eng Engl Transl Tepl. 2015;62(8):598-606. Doi: 10.1134/S0040601515080054

Koschker S, Möst D. Perfect competition vs. strategic behaviour models to derive electricity prices and the influence of renewables on market power. Spectr. 2016;38(3):661-86. Doi: 10.1007/s00291-015-0415-x

Liu G, Zhao JH, Wen F, Yin X, Dong ZY. Option-game-based method for generation investment analysis considering uncertain carbon reduction policy in the electricity market. IET Gener Transm Distrib. 2010;4(8):917-27. Doi: 10.1049/iet-gtd.2009.0439

Zhao Y, Yu Y, Liu H. Transmission constrained market power analysis based on LSFE. Dianli Xitong ZidonghuaAutomation Electr Power Syst. 2003;27(13):30-35.

Kirschen DS, Strbac G, Cumperayot P, De Mendes DP. Factoring the elasticity of demand in electricity prices. IEEE Trans Power Syst. 2000;15(2):612-7. Doi: 10.1109/59.867149

Deng S, Oren S. Electricity derivatives and risk management. Energy. 2006;31(6-7):940-53. Doi: 10.1016/j.energy.2005.02.015

Mandal P, Senjyu T, Urasaki N, Funabashi T, Srivastava AK. A Novel Approach to Forecast Electricity Price for PJM Using Neural Network and Similar Days Method. IEEE Trans Power Syst. 2007;22(4):2058-65. Doi: 10.1109/TPWRS.2007.907386

Rodriguez CP, Anders GJ. Energy Price Forecasting in the Ontario Competitive Power System Market. IEEE Trans Power Syst. 2004;19(1):366-74. Doi: 10.1109/TPWRS.2003.821470

Bojnec S, Papler D. Deregulation of electricity market and drivers of demand for electrical energy in industry. Manag Prod Eng Rev. 2016;7(3):4-10. Doi: 10.1515/mper-2016-0021

García-Álvarez MT, Cabeza-García L, Soares I. An assessment of supply-side and demand-side policies in EU-28 household electricity prices. Int J Sustain Energy Plan Manag. 2020;26(SpecialIssue):5-18. Doi: 10.5278/ijsepm.3417

Westgaard S, Fleten S-E, Negash A, Botterud A, Bogaard K, Verling TH. Performing price scenario analysis and stress testing using quantile regression: A case study of the Californian electricity market. Energy. 2021;214:118796. Doi: 10.1016/j.energy.2020.118796

Afanasyev DO, Fedorova EA, Gilenko EV. The fundamental drivers of electricity price: a multi-scale adaptive regression analysis. Empir Econ. 2020;60(4):1913-1938. Doi: 10.1007/s00181-020-01825-3

Carmona R, Coulon M, Schwarz D. Electricity price modeling and asset valuation: A multi-fuel structural approach. Math Financ Econ. 2013;7(2):167-202. Doi: 10.1007/s11579-012-0091-4

Hakala K, Addor N, Gobbe T, Ruffieux J, Seibert J. Risks and opportunities for a Swiss hydroelectricity company in a changing climate. Hydrol Earth Syst Sci. 2020;24(7):3815-33. Doi: 10.5194/hess-24-3815-2020

Loukatou A, Johnson P, Howell S, Duck P. Optimal valuation of wind energy projects co-located with battery storage. Appl Energy. 2021;283:116247. Doi: 10.1016/j.apenergy.2020.116247

Mikayilov JI, Darandary A, Alyamani R, Hasanov FJ, Alatawi H. Regional heterogeneous drivers of electricity demand in Saudi Arabia: Modeling regional residential electricity demand. Energy Policy. 2020;146:111796. Doi: 10.1016/j.enpol.2020.111796

Dehning P, Blume S, Dér A, Flick D, Herrmann C, Thiede S. Load profile analysis for reducing energy demands of production systems in non-production times. Appl Energy. 2019;237:117-30. Doi: 10.1016/j.apenergy.2019.01.047

Ergemen YE, Haldrup N, Rodríguez-Caballero CV. Common long-range dependence in a panel of hourly Nord Pool electricity prices and loads. Energy Econ. 2016;60:79-96. Doi: 10.1016/j.eneco.2016.09.008

Blumsack S. Impacts of the retirement of the Beaver Valley and Three Mile Island nuclear power plants on capacity and energy prices in Pennsylvania. Electr J. 2018;31(6):57-64. Doi: 10.1016/j.tej.2018.07.004

Cardell JB. Marginal loss pricing for hours with transmission congestion. IEEE Trans Power Syst. 2007;22(4):1466-74. Doi: 10.1109/TPWRS.2007.907123

Garcia M, Baldick R. Approximating Economic Dispatch by Linearizing Transmission Losses. IEEE Trans Power Syst. 2020;35(2):1009-22. Doi: 10.1109/TPWRS.2019.2941906

Asija D, Soni KM, Sinha SK, Yadav VK. Congestion management through determination of congestion zones in price-based electricity markets. J Adv Res Dyn Control Syst. 2018;10(7):95-102.

Zarnikau J, Tsai CH, Woo CK. Determinants of the wholesale prices of energy and ancillary services in the U.S. Midcontinent electricity market. Energy. 2020;195:117051. Doi: 10.1016/j.energy.2020.117051

Zhang F, Wen F, Ngan HW, Yu CW, Chung CY, Wong KP. A multi-round negotiation model for bilateral contracting considering spot price risks in electricity market environment. Dianli Xitong Zidonghua Automation Electr Power Syst. 2010;34(22):29-35.

Ballester C, Furió D. Impact of wind electricity forecasts on bidding strategies. Sustain. 2017;9(8):1318. Doi: 10.3390/su9081318

Zhang ZJ, Nair N-CC. Economic and pricing signals in electricity distribution systems: A bibliographic survey. In: 2012 IEEE International Conference on Power System Technology (POWERCON). Auckland: IEEE; 2012 [cited 2021 Jul 1]. p. 1-6. Doi: 10.1109/PowerCon.2012.6401470

Ferreira ÂP, Ramos JG, Fernandes PO. A linear regression pattern for electricity price forecasting in the Iberian electricity market. Rev Fac Ing. 2019;(93):117-27. Doi: 10.17533/udea.redin.20190522

Sadeghi S, Jahangir H, Vatandoust B, Golkar MA, Ahmadian A, Elkamel A. Optimal bidding strategy of a virtual power plant in day-ahead energy and frequency regulation markets: A deep learning-based approach. Int J Electr Power Energy Syst. 2021;127:106646. Doi: 10.1016/j.ijepes.2020.106646

Mashlakov A, Kuronen T, Lensu L, Kaarna A, Honkapuro S. Assessing the performance of deep learning models for multivariate probabilistic energy forecasting. Appl Energy. 2021;285:116405. Doi: 10.1016/j.apenergy.2020.116405

Ciarreta A, Lagullón M, Zarraga A. Modelación de los precios en el mercado eléctrico español. Cuad Econ. 2011;30:227-50.

Weron R. Electricity price forecasting: A review of the state-of-the-art with a look into the future. Int J Forecast. 2014;30(4):1030-81. Doi: 10.1016/j.ijforecast.2014.08.008

Nowotarski J, Weron R. Recent advances in electricity price forecasting: A review of probabilistic forecasting. Renew Sustain Energy Rev. 2018;81:1548-68. Doi: 10.1016/j.rser.2017.05.234

Mosquera-López S, Manotas-Duque DF, Uribe JM. Risk asymmetries in hydrothermal power generation markets. Electr Power Syst Res. 2017;147:154-64. Doi: 10.1016/j.epsr.2017.02.032

Lisi F, Shah I. Forecasting next-day electricity demand and prices based on functional models. Energy Syst. 2020;11(4):947-79. Doi: 10.1007/s12667-019-00356-w

Kabak M, Tasdemir T. Electricity Day-Ahead Market Price Forecasting by Using Artificial Neural Networks: An Application for Turkey. Arab J Sci Eng. 2020;45(3):2317-26. Doi: 10.1007/s13369-020-04349-1

Jahangir H, Tayarani H, Baghali S, Ahmadian A, Elkamel A, Golkar MA, et al. A Novel Electricity Price Forecasting Approach Based on Dimension Reduction Strategy and Rough Artificial Neural Networks. IEEE Trans Ind Inform. 2020;16(4):2369-81. Doi: 10.1109/TII.2019.2933009

Heydari A, Majidi Nezhad M, Pirshayan E, Astiaso Garcia D, Keynia F, De Santoli L. Short-term electricity price and load forecasting in isolated power grids based on composite neural network and gravitational search optimization algorithm. Appl Energy. 2020;277:115503. Doi: 10.1016/j.apenergy.2020.115503

Zhang Y, Li C, Li L. Wavelet transform and Kernel-based extreme learning machine for electricity price forecasting. Energy Syst. 2018;9(1):113-34. Doi: 10.1007/s12667-016-0227-3

Chen Y, Li M, Yang Y, Li C, Li Y, Li L. A hybrid model for electricity price forecasting based on least square support vector machines with combined kernel. J Renew Sustain Energy. 2018;10(5):055502. Doi: 10.1063/1.5045172

Boltörk E, Oztaysi B. Risk assessment in electricity market integrating value-At-risk approach and forecasting techniques. Int J Ind Eng Theory Appl Pract. 2018;25(4):424-40.

Zolotova IY, Dvorkin VV. Short-term forecasting of prices for the Russian wholesale electricity market based on neural networks. Stud Russ Econ Dev. 2017;28(6):608-15. Doi: 10.1134/S1075700717060144

BP. Statistical Review of World Energy 2020. London, United Kingdom; 2019. Report No.: 69th. Available from: https://www.bp.com/content/dam/bp/business-sites/en/global/corporate/pdfs/energy-economics/statistical-review/bp-stats-review-2020-full-report.pdf

Nagayama H. Effects of regulatory reforms in the electricity supply industry on electricity prices in developing countries. Energy Policy. 2007;35(6):3440-62. Doi: 10.1016/j.enpol.2006.12.018

Samudio-Carter C, Vargas A, Albarracín-Sánchez R, Lin J. Mitigation of price spike in unit commitment: A probabilistic approach. Energy Econ. 2019;80:1041-9. Doi: 10.1016/j.eneco.2019.01.029

Vaca J, Núñez G, Kido A. Análisis multisectorial del incremento de precios de la electricidad en la economía de México. Probl Desarro Rev Latinoam Econ. 2019;50(196):167-89. Doi: 10.22201/iiec.20078951e.2019.196.64775

Enríquez A, Ramírez JC, Rosellón J. Costos de generación, inversión y precios del sector eléctrico en México. Investig Económica. 2019;78(309):58. Doi: 10.22201/fe.01851667p.2019.309.70119

Abos H, Ave M, Martínez-Ortiz H. A case study of a procedure to optimize the renewable energy coverage in isolated systems: an astronomical center in the North of Chile. Energy Sustain Soc. 2017;7(1). Doi: 10.1186/s13705-017-0109-0

Bustos-Salvagno J. Bidding behavior in the Chilean electricity market. Energy Econ. 2015;51:288-99. Doi: 10.1016/j.eneco.2015.07.003

Sikora I, Campos Abad JA, Bustos Salvagno J. Determinantes del precio spot eléctrico en el Sistema Interconectado Central de Chile. Rev Análisis Económico. 2017;32(2):3-38. Doi: 10.4067/S0718-88702017000200003

Barria C, Rudnick H. Investment under Uncertainty in Power Generation: Integrated Electricity Prices Modeling and Real Options Approach. IEEE Lat Am Trans. 2011;9(5):785-92. Doi: 10.1109/TLA.2011.6030990

Lucio NR, Lamas WDQ, De Camargo JR. Strategic energy management in the primary aluminium industry: Self-generation as a competitive factor. Energy Policy. 2013;59:182-8. Doi: 10.1016/j.enpol.2013.03.021

Vahl FP, Rüther R, Casarotto Filho N. The influence of distributed generation penetration levels on energy markets. Energy Policy. 2013;62:226-35. Doi: 10.1016/j.enpol.2013.06.108

Velásquez JD, Dyner I, Souza RC. Modelado del precio spot de la electricidad en Brasil usando una red neuronal autorregresiva. Ingeniare Rev Chil Ing. 2008;16(3):394-403. Doi: 10.4067/S0718-33052008000300002

Xavier EM, Pereira GM, Friedrich LR, Schneider LC, Danesi LC, Borchardt M. Requirements to Leverage the Electricity Distributors' Sales and Revenues in the Brazilian Free Market. IEEE Lat Am Trans. 2016;14(10):4293-303. Doi: 10.1109/TLA.2016.7786308

Rego EE, de Oliveira Ribeiro C, do Valle Costa OL, Ho LL. Thermoelectric dispatch: From utopian planning to reality. Energy Policy. 2017;106:266-77. Doi: 10.1016/j.enpol.2017.03.065

Barrientos J, Rodas E, Velilla E. Modelo para el pronóstico del precio de la energía eléctrica en Colombia. Lect Econ. 2012;(77):91-127.

Díaz-Contreras JA, Macías-Villalba GI, Luna-González E. Estrategia de cobertura con productos derivados para el mercado energético colombiano. Estud Gerenciales. 2014;30(130):55-64. Doi: 10.1016/j.estger.2014.02.008

Lira F, Muñoz C, Núñez F, Cipriano A. Short-term forecasting of electricity prices in the Colombian electricity market. IET Gener Transm Distrib. 2009;3(11):980-6. Doi: 10.1049/iet-gtd.2009.0218

Medina S, Moreno J. Risk evaluation in Colombian electricity market using fuzzy logic. Energy Econ. 2007;29(5):999-1009. Doi: 10.1016/j.eneco.2006.02.008

Quintero-Quintero M del C, Isaza-Cuervo F. Dependencia hidrológica y regulatoria en la formación de precio de la energía en un sistema hidrodominado: caso sistema eléctrico colombiano. Rev Ing Univ Medellín. 2013;12(22):85-96. Doi: 10.22395/rium.v12n22a7