How does a Virtual Power Plant make money?

How does a Virtual Power Plant make money?

This article describes how a virtual power plant creates value by optimizing connected devices. This optimization includes mechanisms like aggregating flexible energy, forecasting and trading, better managing renewable energy sources, and utilization of demand-side response.

The electricity that comes from Solar and wind generation has the lowest cost per MW of any source of electrical power. However, because they are intermittent energy sources, they must be balanced with either storage or flexible energy sources. It is possible to balance RES production by using a virtual power plant.

A virtual power plant consists of a network of optimized and coordinated distributed energy sources, including wind and solar but also biogas, batteries, combined heat and power stations, and many more. In this overview, we'll go into detail regarding how a virtual power plant makes money and provide some answers to the most common questions regarding business cases of virtual power plants.

Aggregation of flexible energy

Bringing together multiple different sources of energy into one system has various advantages for affecting the supply and demand of energy in the system. There are two main sources of value for aggregating flexible power generation, the first is the ancillary services markets. This exists directly in cooperation with the transmission system operator of a given region or country. Ancillary services are compensated to power producers who are able to ramp up or ramp down their power production in a given period of time for a set length of time. In Europe, the standard ramp-up/down periods are defined by the Mari & Picasso platforms. These platforms define how energy and system services need to be cleared, monitored, and remunerated in the EU. The second major source of value is imbalance trading on the intra-day markets. Because forecasts for the amount of energy that will be required are never 100% accurate, energy must be balanced in real time to make up for imperfect predictions.

Forecasting and Trading

The supply of energy in a modern energy system changes throughout the day. This is in part due to the variability of solar and wind power, but also due to the actions of all the power producers and consumers in the energy system. 

Energy is a unique commodity that must be delivered at the same time it is produced because wide-scale storage is not available or financially viable. This is vastly different from virtually every other commodity which can be much easier stored. Because of this unique property, the requirements for the supply of energy are carefully calculated in advance to ensure an uninterrupted supply.

Better data leads to a better forecast. Plant operations, consumption and feed-in measurements, prices and ordered volumes on power exchanges, weather data, and geographic data – all of these data points increase the accuracy of the forecast. From data sources and timeframes to formatting, this forecasting model is complicated. But there's a lot of value in our findings because the data helps to increase efficiency across the wide portfolio of devices. 

For a more detailed explanation of trading, see our knowledge base article.

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Aggregation of Renewable Sources

Inflexible sources of electricity can be traded on ancillary services markets. This is done by forecasting the available amount of energy on the day-ahead markets. In order to ensure that there will be enough available only part of the available power is bid, based on the level of confidence of the prediction. Solar and wind are inflexible in the sense that the energy produced by them depends on the weather, however, they can be curtailed depending on the particular installation. When renewable energy can be provably and successfully shut down based on the needs of the grid operator they are able to pay for that action. The system depends first on a reliable and accurate model to predict production on the basis of the day ahead. The amount of electricity available for control at a given time needs to be accurate because there are large risks if the energy is not able to be controlled when the requirement is given by the grid operator. The coupling of such systems with battery storage or to other power units that are able to be dispatched greatly improves the ability to offer RES units on flexibility markets.

There are four main sources of value from RES data:

  • By using data analytics and machine learning algorithms, renewable energy companies can better forecast the amount of energy that will be produced at any given time, and reduce the need for balancing costs.
  • When prices are negative RES sources can be curtailed, which brings the price back to equilibrium, and lowers overall system costs.
  • In cases where RES are dispatchable such as hydropower or biogas, they can be used to generate energy when necessary
  • A better understanding of the RES market gives us a better picture of overall energy values since prices are closely correlated. The trader can use forecasts from RES production to take a safer and more lucrative trading position in the intraday markets overall. 

Demand Response aggregation

From the position of the market operator, the way that energy is balanced works virtually the same if either power is ramped up at a generation station or power consumption is lowered at an industrial facility such as for example, a cold storage operator who is able to temporarily turn off their cooling system. Therefore demand response is one of the most promising forms of balancing the supply of energy because the costs of curtailing energy use may be significantly lower than the costs of producing additional units of electricity. Demand response is addressed on an individual basis because the needs of each unique business differ. Specifically, how much energy is available to be curtailed, how long it can be curtailed - without negatively affecting operations, and the specifics associated with controlling individual machines. 

There is a vast amount of production facilities in Europe that are capable of participating in demand response programs. Virtually any business that uses large amounts of electricity that can be either temporarily shut down, or replaced by its own production is a potential candidate for demand-side response programs. 

Learn more about demand response

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Virtual Power Plants for the residential sector and local energy communities

There is increasing interest in optimizing home energy use via time-of-use tariffs. In addition, giving prosumers who operate their own energy production, most commonly in the form of rooftop solar, the ability to sell and share their energy in local energy communities. Virtual power plants operating on the residential level are a relatively new type of business. The opportunity to manage energy use on the home level has a variety of ways for reducing the cost of energy and creating value by shifting energy use over time. 

Some of the ways that managing home energy use can create value include, shifting energy consumption for large appliances to times when costs are low, reducing imbalance fees for the energy provider, managing energy better in networks with limited capacity, as well as participating in ancillary services markets by managing energy production and energy storage on the home level. 

There are many more ways to find value in better managing home energy use. Our sister company Nano Green is an expert in this area.

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There is a huge opportunity to create a more resilient and low-carbon energy system provided by virtual power plants. Nano Energies can help you to create additional value from the way you use or create energy. We will work closely with you to understand your business, create an operational plan based on your needs and preferences and show you the value of using your assets to provide energy flexibility.

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