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Electricity pricing

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Electricity pricing (also referred to as electricity tariffs or the price of electricity) can vary widely by country or by locality within a country. Electricity prices are dependent on many factors, such as the price of power generation, government taxes or subsidies, CO
2 taxes,[1] local weather patterns, transmission and distribution infrastructure, and multi-tiered industry regulation. The pricing or tariffs can also differ depending on the customer-base, typically by residential, commercial, and industrial connections.

Electricity transport via high-voltage line

According to the U.S. Energy Information Administration (EIA), "Electricity prices generally reflect the cost to build, finance, maintain, and operate power plants and the electricity grid." Where pricing forecasting is the method by which a generator, a utility company, or a large industrial consumer can predict the wholesale prices of electricity with reasonable accuracy.[2] Due to the complications of electricity generation, the cost to supply electricity varies minute by minute.[3]

Some utility companies are for-profit entities and their prices include a financial return for owners and investors. These utility companies can exercise their political power within existing legal and regulatory regimes to guarantee a financial return and reduce competition from other sources like a distributed generation.[4]

Rate structure

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In standard regulated monopoly markets like the United States, there are multilevel governance structures that set electricity rates. The rates are determined through a regulatory process that is overseen by governmental organizations.

The inclusion of renewable energy distributed generation (DG) and advanced metering infrastructure (AMI or smart meter) in the modern electricity grid has introduced many alternative rate structures.[5] There are several methods that modern utilities structure residential rates:

  • Simple (or fixed) – the rate at which customers pay a flat rate per kWh
  • Tiered (or step) – rate changes with the amount of use (some go up to encourage energy conservation, others go down to encourage use and electricity provider profit)
  • Time of use (TOU) – different rate depending on the time of day
  • Demand rates – based on the peak demand for electricity a consumer uses
  • Tiered within TOU – different rates depending on how much they use at a specific time of day
  • Seasonal rates – charged for those that do not use their facilities year-round (e.g. a cottage)
  • Weekend/holiday rates – generally different rates than during normal times. among the few residential rate structures offered by modern utilities.

The simple rate charges a specific dollar per kilowatt hour ($/kWh) consumed. The tiered rate is one of the more common residential rate programs. The tiered rate charges a higher rate as customer usage increases. TOU and demand rates are structured to help maintain and control a utility's peak demand.[6] The concept at its core is to discourage customers from contributing to peak-load times by charging them more money to use power at that time. Historically, rates have been minimal at night because the peak is during the day when all sectors are using electricity. Increased demand requires additional energy generation, which is traditionally provided by less efficient "peaker" plants that cost more to generate electricity than "baseload" plants.[7] However, as greater penetration from renewable energy sources, like solar, are on a grid the lower cost, electricity is shifted to midday when solar generates the most energy. Time of use (TOU) tariffs can shift electricity consumption out of peak periods, thus helping the grid cope with variable renewable energy.[8][9]

A feed-in tariff (FIT)[10] is an energy-supply policy that supports the development of renewable power generation. FITs give financial benefits to renewable power producers. In the United States, FIT policies guarantee that eligible renewable generators will have their electricity purchased by their utility.[11] The FIT contract contains a guaranteed period of time (usually 15–20 years) that payments in dollars per kilowatt hour ($/kWh) will be made for the full output of the system.

Net metering is another billing mechanism that supports the development of renewable power generation, specifically, solar power. The mechanism credits solar energy system owners for the electricity their system adds to the grid. Residential customers with rooftop photovoltaic (PV) systems will typically generate more electricity than their home consumes during daylight hours, so net metering is particularly advantageous. During this time where generation is greater than consumption, the home's electricity meter will run backward to provide a credit on the homeowner's electricity bill.[12] The value of solar electricity is less than the retail rate, so net metering customers are actually subsidized by all other customers of the electric utility.[13]

United States: the Federal Energy Regulatory Commission (FERC) oversees the wholesale electricity market along with the interstate transmission of electricity. Public Service Commissions (PSC), which are also known as Public utilities commission (PUC), regulate utility rates within each state.

Price comparison by power source

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Main article: Cost of electricity by source

The cost of electricity also differs by the power source. The net present value of the unit-cost of electricity over the lifetime of a generating asset is known as the levelized cost of electricity (LCOE). LCOE is the best value to compare different methods of generation on a consistent basis.[citation needed]

The generating source mix of a particular utility will thus have a substantial effect on their electricity pricing. Electric utilities that have a high percentage of hydroelectricity will tend to have lower prices, while those with a large amount of older coal-fired power plants will have higher electricity prices. Recently the LCOE of solar photovoltaic technology[14] has dropped substantially.[15][16] In the United States, 70% of current coal-fired power plants run at a higher cost than new renewable energy technologies (excluding hydro) and by 2030 all of them will be uneconomic.[17] In the rest of the world 42% of coal-fired power plants were operating at a loss in 2019.[17]

Electricity price forecasting

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This section is an excerpt from Electricity price forecasting.[edit]

Electricity price forecasting (EPF) is a branch of energy forecasting which focuses on using mathematical, statistical and machine learning models to predict electricity prices in the future. Over the last 30 years electricity price forecasts have become a fundamental input to energy companies’ decision-making mechanisms at the corporate level.[18]

Since the early 1990s, the process of deregulation and the introduction of competitive electricity markets have been reshaping the landscape of the traditionally monopolistic and government-controlled power sectors. Throughout Europe, North America, Australia and Asia, electricity is now traded under market rules using spot and derivative contracts.[19] However, electricity is a very special commodity: it is economically non-storable and power system stability requires a constant balance between production and consumption. At the same time, electricity demand depends on weather (temperature, wind speed, precipitation, etc.) and the intensity of business and everyday activities (on-peak vs. off-peak hours, weekdays vs. weekends, holidays, etc.). These unique characteristics lead to price dynamics not observed in any other market, exhibiting daily, weekly and often annual seasonality and abrupt, short-lived and generally unanticipated price spikes.[20]

Extreme price volatility, which can be up to two orders of magnitude higher than that of any other commodity or financial asset, has forced market participants to hedge not only volume but also price risk. Price forecasts from a few hours to a few months ahead have become of particular interest to power portfolio managers. A power market company able to forecast the volatile wholesale prices with a reasonable level of accuracy can adjust its bidding strategy and its own production or consumption schedule in order to reduce the risk or maximize the profits in day-ahead trading.[21] A ballpark estimate of savings from a 1% reduction in the mean absolute percentage error (MAPE) of short-term price forecasts is $300,000 per year for a utility with 1GW peak load. With the additional price forecasts, the savings double.[22]

Power quality

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Excessive Total Harmonic Distortions (THD) and low power factor are costly at every level of the electricity market. The impact of THD is difficult to estimate, but it can potentially cause heat, vibrations, malfunctioning and even meltdowns. The power factor is the ratio of real to apparent power in a power system. Drawing more current results in a lower power factor. Larger currents require costlier infrastructure to minimize power loss, so consumers with low power factors get charged a higher electricity rate by their utility.[23] Power quality is typically monitored at the transmission level. A spectrum of compensation devices[24] mitigate bad outcomes, but improvements can be achieved only with real-time correction devices (old style switching type,[25] modern low-speed DSP driven[26] and near real-time[27]). Most modern devices reduce problems, while maintaining return on investment and significant reduction of ground currents. Power quality problems can cause erroneous responses from many kinds of analog and digital equipment.

See also

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References

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  1. ^ Stanley Reed (22 September 2021). "Here's What's Behind Europe's Surging Energy Prices". The New York Times. Retrieved 24 September 2021. High carbon taxes are also stoking power prices
  2. ^ Weron, Rafał (2014). "Electricity price forecasting: A review of the state-of-the-art with a look into the future". International Journal of Forecasting. 30 (4): 1030–1081. doi:10.1016/j.ijforecast.2014.08.008.
  3. ^ "Factors Affecting Electricity Prices – Energy Explained, Your Guide To Understanding Energy – Energy Information Administration". www.eia.gov. Retrieved 3 May 2018.
  4. ^ Prehoda, Emily; Pearce, Joshua; Schelly, Chelsea (2019). "Policies to Overcome Barriers for Renewable Energy Distributed Generation: A Case Study of Utility Structure and Regulatory Regimes in Michigan". Energies. 12 (4): 674. doi:10.3390/en12040674.
  5. ^ Zheng, Junjie; Lai, Chun Sing; Yuan, Haoliang; Dong, Zhao Yang; Meng, Ke; Lai, Loi Lei (July 2020). "Electricity plan recommender system with electrical instruction-based recovery". Energy. 203: 117775. doi:10.1016/j.energy.2020.117775. S2CID 219466165.
  6. ^ Torriti, Jacopo. "Appraising the Economics of Smart Meters".
  7. ^ Fetchen, Stephanie (12 September 2019). "Growing Renewable Generation Causing Changes In Generation Charges". RateAcuity. Retrieved 15 October 2019.
  8. ^ "Smart time-of-use tariff shows "significant impact" on energy consumption behaviour". SMS plc. 31 October 2018. Retrieved 20 September 2021.
  9. ^ "Electricity Retail Rate Design in a Decarbonizing Economy: An Analysis of Time-of-Use and Critical Peak Pricing -". CEEPR. Retrieved 21 September 2023.
  10. ^ Couture, T. D.; Cory, K.; Kreycik, C.; Williams, E. (1 July 2010). "Policymaker's Guide to Feed-in Tariff Policy Design". doi:10.2172/984987. OSTI 984987. {{cite journal}}: Cite journal requires |journal= (help)
  11. ^ "Feed-in Tariff Resources | Department of Energy". www.energy.gov. Archived from the original on 4 May 2018. Retrieved 3 May 2018.
  12. ^ "Net Metering | SEIA". SEIA. Retrieved 3 May 2018.
  13. ^ Rethinking The rationale for Net Metering: Quantifying subsidy from non-solar to solar customers. Alexander, Brown, and Faruqui. http://ipu.msu.edu/wp-content/uploads/2017/09/Rethinking-Rationale-for-Net-Metering-2016.pdf
  14. ^ Branker, K.; Pathak, M.J.M.; Pearce, J.M. (2011). "A review of solar photovoltaic levelized cost of electricity". Renewable and Sustainable Energy Reviews. 15 (9): 4470–4482. doi:10.1016/j.rser.2011.07.104. hdl:1974/6879. S2CID 73523633.
  15. ^ Lai, Chun Sing; McCulloch, Malcolm D. (2017). "Levelized cost of electricity for solar photovoltaic and electrical energy storage". Applied Energy. 190: 191–203. doi:10.1016/j.apenergy.2016.12.153. S2CID 113623853.
  16. ^ Kang, Moon Hee; Rohatgi, Ajeet (2016). "Quantitative analysis of the levelized cost of electricity of commercial scale photovoltaics systems in the US". Solar Energy Materials and Solar Cells. 154: 71–77. doi:10.1016/j.solmat.2016.04.046.
  17. ^ a b "42% of global coal power plants run at a loss, finds world-first study". Carbon Tracker Initiative. 30 November 2018. Retrieved 14 March 2019.
  18. ^ Maciejowska, Katarzyna; Uniejewski, Bartosz; Weron, Rafal (19 July 2023), "Forecasting Electricity Prices", Oxford Research Encyclopedia of Economics and Finance, Oxford University Press, arXiv:2204.11735, doi:10.1093/acrefore/9780190625979.013.667, ISBN 978-0-19-062597-9, retrieved 12 April 2024
  19. ^ Mayer, Klaus; Trück, Stefan (March 2018). "Electricity markets around the world". Journal of Commodity Markets. 9: 77–100. doi:10.1016/j.jcomm.2018.02.001.
  20. ^ Weron, Rafał (2014). "Electricity price forecasting: A review of the state-of-the-art with a look into the future". International Journal of Forecasting. 30 (4). [Open Access]: 1030–1081. doi:10.1016/j.ijforecast.2014.08.008.
  21. ^ Weron, Rafał (2006). Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach. Wiley. ISBN 978-0-470-05753-7.
  22. ^ Hong, Tao (2015). "Crystal Ball Lessons in Predictive Analytics". EnergyBiz Magazine. Spring: 35–37. Archived from the original on 10 September 2015. Retrieved 29 November 2015.
  23. ^ "MCMA – Motion Control Online". MCMA – Motion Control Online. Retrieved 3 May 2018.
  24. ^ "Practical Power Factor Correction : Power Factor – Electronics Textbook". All About Circuits. Retrieved 18 June 2017.
  25. ^ "High Speed Real Time Automatic Power Factor Correction System" (PDF). Archived from the original (PDF) on 29 April 2016. Retrieved 18 June 2017.
  26. ^ "TCI, LLC – HGA 5% Active Harmonic Filter". TransCoil. Retrieved 18 June 2017.
  27. ^ "3DFS Software Defined Power". 3DFS. Retrieved 18 June 2017.
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