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Highlights
- The power sector faces challenges by way of changing consumer demand, an emphasis on renewable energy, an evolving market structure, and increased market participation. All of this makes trading in the electricity market complex.
- In a “day-ahead” real-world electricity market, sellers and buyers submit their offers and bids a day in advance before the actual delivery of electricity takes place. A digital simulation of a “day-ahead” energy market helps understand price dynamics in different scenarios in a real market.
- This paper explores the simulation of a virtual electricity market ecosystem, comprising electricity buyers, sellers, distributors and energy companies, and network operators. It also assesses the impact of weather as one of the factors in deciding electricity market pricing and participation.
- An ecosystem modeling approach in such a “day-ahead” digital simulation of the market looks at “what-if” scenarios to analyze the players in this ecosystem. The model then generates intelligent insights to help stakeholders make an informed decision, eliminating the need for a trial-and-error approach in energy trading.
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Understanding the electricity markets
Electricity is unique as a trading commodity because it cannot be generated in advance or stored.
This makes trading in an energy market much more layered and complex compared to trade in other commodities markets. There is daily volatility in electricity demand and supply and several entities are involved apart from just a buyer and seller.
Pricing in the energy trade is dependent on variables such as weather, daily energy demand and supply, the state of distributed energy sources such as solar rooftops or battery storage systems, and the available capacity in transmission lines.
Power generation or supply depends on three fundamental parameters–the availability of fuel (fossil or renewable energy), the availability of plant generators, and constraints such as the ramp-up (increase) and ramp-down (decrease) rates of a plant’s output.
Furthermore, energy trading happens across different time horizons, such as “day-ahead” and “intra-day” to manage pricing dynamics in the market.
A “day-ahead” energy market functions with a single-auction window with uniform pricing, while “intra-day” is a continuous pay-as-you-bid market.
How the “day-ahead” energy market works
In a “day-ahead” market, sellers and buyers submit their offers and bids 24 hours before delivery day to a market or system operator.
An offer implies how much power they’re willing to sell, and bids are the price at which buyers will make a purchase.
Deciding the "day-ahead" electricity market price
Factors that drive pricing when trading in a “day-ahead”—electricity market
Deciding the "day-ahead" electricity market price
A market operator does the bid matching based on the defined business rules in the “day-ahead” market. A market clearing rule is based on the intersection of supply and demand curves to decide a market clearing price and quantity. The respective curves are constructed by listing prices—in increasing order for sell offers and in decreasing order for buy bids, corresponding to the quantity. The market operator then publishes or shares the market clearing results (price and quantity for each time-block of the next day) with all market participants.
However, there are limitations and challenges when trading happens a day ahead in real time, making a compelling case to study and model the electricity ecosystem digitally.
Digitally simulating an electricity market
A digitally simulated electricity ecosystem provides valuable insights and reduces time needed to gain experience.
Given that a day-ahead market in real-world trading has many layers and still presents only an approximation of what can be, informed decision making is the single most significant differentiator that a digital simulation offers.
To develop such a simulation, an approach called ecosystem modeling is used. The virtual ecosystem “mimics” or simulates an electricity market, using various “what-if” scenarios, to generate insights that help stakeholders make more informed and accurate decisions. Users can ask varied questions based on the simulated setup.
The ecosystem modeling approach helps understand the power system behavior (including transmission and distribution systems)—in which each entity or agent and their interconnections and interactions are modeled—to form a virtual ecosystem or a digital ecosystem twin. A market participant can partake in this virtual market to study and analyze trends from varied entity perspectives—that of a seller, buyer, market/system operator, and aggregator—to better predict market trades.
The process and flow of information can be supported by analytics based on operations research, machine learning, or domain models. This helps a user make informed decisions, eliminating the use of trial-and-error, in the real world.
Weather as a variable in a digitally simulated energy market
Renewable resources are changing how the electricity markets operate given the need for sustainable sources of power.
Weather influences demand and renewable energy supply. For example: power usage when appliances such as heaters or air conditioners are turned on. Similarly, wind speed and cloud cover also play a vital role when solar or wind energy is used as fuel for renewable power generation. Distributed energy resources—such as rooftop solar panels—also depend on weather and can alter the net demand of a customer.
An ecosystem modeling approach can factor in weather as a principal agent and can assess its singular impact on market prices.
Digitally simulated electricity market ecosystem
What ecosystem modeling looks like for a “day-ahead” electricity market using weather as an agent
Digitally simulated electricity market ecosystem
In such a scenario, a weather agent is a fictional entity, potentially a software or a website, that provides information on temperature, humidity, air quality, precipitation, barometric pressure, and wind speed. This agent helps create multiple scenarios with its forecasting abilities. Based on the forecast, a seller or a buyer can effectively bet their trades, estimating demand and supply needs in advance.
Similarly, an aggregator can club both demand and supply bids to participate in the market, as distributed energy resources are dual in nature. For instance, when charging, a battery acts as a buyer, but functions as a supplier when discharging.
A simulation model will prove beneficial to deliver timely and accurate decisions to an energy market in the backdrop of rapidly evolving policies and business rules given the pace of technology-driven change in the industry.
