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.
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. 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.
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. 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.
Forecasting methodology The simplest model for day ahead forecasting is to ask each generation source to bid on blocks of generation and choose the cheapest bids. If not enough bids are submitted, the price is increased. If too many bids are submitted the price can reach zero or become negative. The offer price includes the generation cost as well as the transmission cost, along with any profit. Power can be sold or purchased from adjoining power pools.ISO NE
The concept of independent system operators (ISOs) fosters competition for generation among wholesale market participants by unbundling the operation of transmission and generation. ISOs use bid-based markets to determine economic dispatch.
Wind and solar power are non-dispatchable. Such power is normally sold before any other bids, at a predetermined rate for each supplier. Any excess is sold to another grid operator, or stored, using pumped-storage hydroelectricity, or in the worst case, curtailed. Curtailment could potentially significantly impact solar power's economic and environmental benefits at greater PV penetration levels. Allocation is done by bidding.
The effect of the recent introduction of smart grids and integrating distributed renewable generation has been increased uncertainty of future supply, demand and prices. This uncertainty has driven much research into the topic of forecasting.
Driving factors Electricity cannot be stored as easily as gas, it is produced at the exact moment of demand. All of the factors of supply and demand will, therefore, have an immediate impact on the price of electricity on the spot market. In addition to production costs, electricity prices are set by supply and demand. However, some fundamental drivers are the most likely to be considered.
Short-term prices are impacted the most by the weather. Demand due to heating in the winter and cooling in the summer are the main drivers for seasonal price spikes. Additional natural-gas fired capacity is driving down the price of electricity and increasing demand.
A country's natural resource endowment, as well as its regulations in place greatly influence tariffs from the supply side. The supply side of the electricity supply is most influenced by fuel prices, and CO2 allowance prices. The EU carbon prices have doubled since 2017, making it a significant driving factor of price.
Weather Studies show that demand for electricity is driven largely by temperature. Heating demand in the winter and cooling demand (air conditioners) in the summer are what primarily drive the seasonal peaks in most regions. Heating degree days and cooling degree days help measure energy consumption by referencing the outdoor temperature above and below 65 degrees Fahrenheit, a commonly accepted baseline.
In terms of renewable sources like solar and wind, weather impacts supply. California's duck curve shows the difference between electricity demand and the amount of solar energy available throughout the day. On a sunny day, solar power floods the electricity generation market and then drops during the evening, when electricity demand peaks.
Forecasting for wind and solar renewable energy is becoming more important as the amount of energy generated from these sources increases. Meteorological forecasts can improve the accuracy of electricity price forecasting models. While day-ahead forecasts can take advantage of autoregressive effects, forecasts featuring meteorological data are more accurate for 2-4 day-ahead horizons. In some cases, renewable energy generation forecasts published by Transmission System Operators (TSOs) can be improved with simple prediction models and used provide more accurate electricity price predictions.
Hydropower availability Snowpack, streamflows, seasonality, salmon, etc. all affect the amount of water that can flow through a dam at any given time. Forecasting these variables predicts the available potential energy for a dam for a given period. Some regions such as Pakistan, Egypt, China and the Pacific Northwest get significant generation from hydroelectric dams. In 2015, SAIDI and SAIFI more than doubled from the previous year in Zambia due to low water reserves in their hydroelectric dams caused by insufficient rainfall.J. Arlet. 2017. "Electricity sector constraints for firms across economies : a comparative analysis." Doing business research notes; no.1. Washington, D.C. : World Bank Group. http://documents.worldbank.org/curated/en/409771499690745091/Electricity-sector-constraints-for-firms-across-economies-a-comparative-analysis p.10
Power plant and transmission outages Whether planned or unplanned, outages affect the total amount of power that is available to the grid. Outages undermine electricity supply, which in turn affects the price.
Economic health During times of economic hardship, many factories cut back production due to a reduction of consumer demand and therefore reduce production-related electrical demand.
Government regulation Governments may choose to make electricity tariffs affordable for their population through subsidies to producers and consumers. Most countries characterized as having low energy access have electric power utilities that do not recover any of their capital and operating costs, due to high subsidy levels.C. Trimble, M. Kojima, I. Perez Arroyo, F. Mohammadzadeh. 2016. "Financial Viability of Electricity Sectors in Sub-Saharan Africa: Quasi-Fiscal Deficits and Hidden Costs." Policy Research Working Paper; No. 7788.
Taxonomy of modeling approaches
A variety of methods and ideas have been tried for electricity price forecasting (EPF), with varying degrees of success. They can be broadly classified into six groups.
Multi-agent models Multi-agent (multi-agent simulation, equilibrium, game theoretic) models simulate the operation of a system of heterogeneous agents (generating units, companies) interacting with each other, and build the price process by matching the demand and supply in the market. This class includes cost-based models (or production-cost models, PCM), equilibrium or game theoretic approaches (like the Nash-Cournot framework, supply function equilibrium - SFE, strategic production-cost models - SPCM) and agent-based models.
Multi-agent models generally focus on qualitative issues rather than quantitative results. They may provide insights as to whether or not prices will be above marginal costs, and how this might influence the players' outcomes. However, they pose problems if more quantitative conclusions have to be drawn, particularly if electricity prices have to be predicted with a high level of precision.
Fundamental models Fundamental (structural) methods try to capture the basic physical and economic relationships which are present in the production and trading of electricity. The functional associations between fundamental drivers (loads, weather conditions, system parameters, etc.) are postulated, and the fundamental inputs are modeled and predicted independently, often via statistical, reduced-form or computational intelligence techniques. In general, two subclasses of fundamental models can be identified: parameter rich models and parsimonious structural models of supply and demand.
Two major challenges arise in the practical implementation of fundamental models: data availability and incorporation of stochastic fluctuations of the fundamental drivers. In building the model, we make specific assumptions about physical and economic relationships in the marketplace, and therefore the price projections generated by the models are very sensitive to violations of these assumptions.
Reduced-form models Reduced-form (quantitative, stochastic) models characterize the statistical properties of electricity prices over time, with the ultimate objective of derivatives valuation and risk management. Their main intention is not to provide accurate hourly price forecasts, but rather to replicate the main characteristics of daily electricity prices, like marginal distributions at future time points, price dynamics, and correlations between commodity prices. If the price process chosen is not appropriate for capturing the main properties of electricity prices, the results from the model are likely to be unreliable. However, if the model is too complex, the computational burden will prevent its use on-line in trading departments. Depending on the type of market under consideration, reduced-form models can be classified as: Spot price models, which provide a parsimonious representation of the dynamics of spot prices. Their main drawback is the problem of pricing derivatives, i.e., the identification of the risk premium linking spot and forward prices. The two most popular subclasses include jump-diffusion and Markov regime-switching models. Forward price models allow for the pricing of derivatives in a straightforward manner (but only of those written on the forward price of electricity). However, they too have their limitations; most importantly, the lack of data that can be used for calibration and the inability to derive the properties of spot prices from the analysis of forward curves.
Statistical models