Flexibility potential: Flexibility potential in the electricity market refers to the willingness of residential consumers to adjust their electricity usage in response to supply conditions, price signals, or grid needs. It enables demand-side participation, supporting grid stability, integrating renewables, and reducing costs through load shifting, curtailment, or storage. The flexibility potential of a certain customer is to providde energy when it is needed 

Aggregator: The agentfigure of the independent aggregator is still at an early stage of integration into the electricity market ecosystem. The EU defines an aggregator as a group of agents (or customers) in the electricity grid which acts like a single entity when operating in the electricity market. The aggregator gathers the flexibility potential of its members and sells this potential to the system operator, helping to reduce grid congestions (and therefore deferring the need for network reinforcement and minimising the necessity of balance energy supply, typically powered by fossil fuels and increasing the cost in the electricity bill). 

TSO/DSO: Transmission System Operators (TSOs) and Distribution System Operators (DSOs) are those entities responsible for the correct operation and maintenance of the transport and distribution grids respectively. 

• In Spain: TSO: REE; DSOs: Iberdrola, Endesa, Naturgy among others 

• In Germany: TSOs: 50Hertz, TenneT…; DSOs:Westnetz, Netze BW, Bayernwerk Netz… 

• In Greece: TSO:IPTO; DSO: HEDNO 

Prosumer: A prosumer is an individual or entity that both produces and consumes electricity, often generating renewable energy through systems like solar panels and selling excess power back to the grid, but also a user involved in a demand response programme which offers flexibility to the grid. 

End-consumer: In the electricity market, the end consumer is the final customer who will use the energy. While aggregators and other electricity suppliers act like intermediaries and go to the electricity market to buy electricity, end-consumers are the final users who will actually use the electricity and pay to the intermediaries for their service. 

Demand Response Event: A demand response event is an event the aggregator sends to the end-consumers to adjust their energy consumption during a specific amount of time (for example, to encourage using the washing machine anytime but between 2:00 pm and 5:00 pm, telling the potential benefit it would have both economical and environmental if complying or just giving advice that those are the more expensive hours of the day to do so and offering better alternatives like the cheaper hours at the day to switch this consumption they would have had during the event). This event can be followed manual, if the aggregator notifies the end-consumer in their respective apps, or also automatic if the end usser accounts for smart appliances and allows aggregator to modify the consumption of some of those appliances for providing demand flexibility. 

Demand Response (DR) is, according to the International Energy Agency (IEA), a concept that “refers to balancing the demand on power grids by encouraging customers to shift electricity demand to times when electricity is more plentiful or other demand is lower, typically through prices or monetary incentives. Along with smart grids and energy storage, demand response is an important source of flexibility for managing the impact of variable renewables and growing electricity demand on the stability and reliability of electricity grids.” 

For a better understanding, we have asked ChatGPT to give an easy-to-understand definition for this concept. The answer to the question “Explain what is Demand Response to a non-expert in the electricity field” was:  

“Imagine it’s a super hot day, and everyone in your neighbourhood turns on their air conditioners at the same time. This creates a huge demand for electricity all at once, which can be tough for the power company to handle. If the power company can’t keep up, there might be blackouts where the electricity goes out. 

Now, think of Demand Response like a group project where the power company asks for volunteers to help manage this situation. The power company sends out a signal or a message saying, “Hey, we need some help reducing electricity use right now. Can anyone turn off their AC for a little bit or use less power?” 

Some people and businesses have agreed ahead of time to do this, and they might have special thermostats or systems that automatically adjust when they get the signal. In return for helping out, they might get some benefits like credits on their electricity bill or other rewards. 

By participating in Demand Response, these volunteers help balance the demand for electricity with the available supply. This prevents blackouts and keeps the electricity grid stable. It’s like when a teacher asks the class to calm down and work together so everyone can focus and get their work done. When people respond to the call for lower power usage, it’s like the class working together to create a better environment for everyone. 
So, Demand Response is basically a teamwork strategy between the power company and its customers to make sure there’s enough electricity to go around, especially during times when it’s needed the most.” 

So, briefly, Demand Response is a strategy that system operators can follow to regulate the energy consumption of the end users (through an aggregator in the case of residential DR or through a DR programme for bigger consumers). The idea is to allow SOs to balance the system without needing to use other conventional sources, which would mean an increase in the final price of electricity.

How does Demand Response work? Different DR approaches. 

With the aforementioned definition of Demand Response and from an end-user perspective is relatively easy to think about the necessity of taking place in the electricity market as an individual market agent in order to perform those DR activities. Nonetheless, individual customers are not suitable for participation in the wholesale electricity market due to the requirements of market operators for becoming an individual market participant. Instead, to unlock the potential of flexibility of individual houses the role of aggregators emerged as a cornerstone for the implementation of DR strategies in the electricity market. Those are companies responsible for aggregating the flexibility of several individual customers into a single block of energy and act as an intermediary between market operators and end-users. 

To correctly implement these measures, improving the performance of all agents involved in electricity generation and transmission while showing it as an attractive alternative for the end-users, demand response counts with two different approaches: 

• Incentive-based demand response: In this approach, end-users are called off when energy distributors need help to optimise the grid, either because they are facing high demand hours or because some generators have modified their production schedule downwards. This call is typically made by an aggregator, an agent who is an intermediate between end users and DSOs-TSOs and oversees the flexibility to be provided, helping to mitigate the effect of such phenomena. An aggregator accounts for several end-users in its portfolio so it divides the flexibility needed into smaller amounts, affordable for each one of the houses. 

• Price-based demand response: whereas incentive-based DR requires from external agents to set some goals or constraints regarding the consumption of a household, price-based demand response focus on the opportunity of savings when a dynamic electricity tariff is available. In this scenario, leveraging our consumption to avoid peak hours lead to significant savings in the electricity bill. Furthermore, those peak hours tend to be those with higher stress for the grid, so it helps to reduce those undesirable conditions. 

As shown in Error! Reference source not found., for incentive-based Demand Response programs, system operators make a call to the aggregators at moments of high demand or high grid stress asking for help to mitigate those negative effects. Then, aggregators apply for the quantity of flexibility their customers are willing to give, and send those demand response signals to  end-users involved in demand response programs through a notification in their linked devices. In exchange for this flexibility offered, end users earn compensation in their electricity bill or other rewards. 

For system operators, Demand Response is also a powerful ally. Reducing peak-hour electricity demand is the more obvious demand response application in hours of great stress due to technical contingencies or high demand of the users (as previously appointed for the most common reasons for DR incentive-based signals), but in the future, system operators may also send demand response signals to provide more stability to the electricity grid by flattening the demand curve. Error! Reference source not found. highlights how demand response events can help with this task by reducing peak demand (curtailment), shifting loads from peak hours (typically those with high demand) to others or simply increasing the energy consumption on valley hours where consumption is too low. This last feature becomes specially interesting looking at the near future with energy storage technologies in the spotlight of the energy field. 

Learning with a practical example 

Once technical aspects and different approaches for Demand response have been defined, let’s set up an example of real implementation of demand response actions or signals. 

For this example, imagine a house with smart-metering and access to both dynamic price electricity tariffs and an aggregator energy supplier. The house presents an average consumption of 15 kWh/day coming from different appliances such as: oven, refrigerator, lightning, HVAC system (air conditioner), electric water heater, dish washer and washing machine. 

Notice how a few number of these appliances are not suitable for offering flexibility to the system (refrigerator, lightning) whereas most of them are customizable in terms of possible periods of use.  

For an average energy price of: 

• 0,30 €/kWh for peak hours (10:00h-14:00h and 18:00h-22:00h) 

• 0,22 €/kWh for flat hours (08:00h-10:00h; 14:00h-18:00h and 22:00h-00:00h) 

• 0,08 €/kWh for valley hours (00:00h-08:00h and all the weekend) 

And assuming a low impact of demand flexibility (60% energy consumption on peak hours, 30% flat hours and just 10% in valley) would result in an electricity bill of: 

0,30 €/kWh peak∗15 kWh/day∗30 days/month∗ 0,6 peak hour consumption/total consumption=81 €/month0,30 €/kWh peak∗15 kWh/day∗30 days/month∗ 0,6 peak hour consumption/total consumption=81 €/month

0,22 €/kWh flat∗15 kWh/day∗30 days/month∗ 0,3 flat hour consumption/total consumption=29,7 €/month0,22 €/kWh flat∗15 kWh/day∗30 days/month∗ 0,3 flat hour consumption/total consumption=29,7 €/month

0,08 €/kWh valley∗15 kWh/day∗30 days/month∗ 0,1 flat hour consumption/total consumption=3,6 €/month0,08 €/kWh valley∗15 kWh/day∗30 days/month∗ 0,1 flat hour consumption/total consumption=3,6 €/month

Which results in an average electricity bill of 114,3 €/month in the energy term (without taxes, tolls and power costs which remains constant). 

For price-based demand response assuming access to these Time of Use tariffs (on day D, price is set up for each one of the 24 hours of the next day D+1), being able to switch consumption patterns may lead to energy savings and is considered as a Demand response act.  

For example, a smart electricity water heater, heating water during the night to use it in the next day, using the oven in flat hours instead of peak hours, or doing batch cooking during the weekend would change the total amount of energy we use during peak/flat/valley hours from the original 60%, 30% and 10% to a better scenario like 30% peak hour consumption, 40% flat hour consumption and 30% valley hour consumption, resulting in a new electricity bill accounting for: 

0,30 €/kWh peak∗15 kWh/day∗30 days/month∗ 0,3 peak hour consumption/total consumption=40,5 €/month0,30 €/kWh peak∗15 kWh/day∗30 days/month∗ 0,3 peak hour consumption/total consumption=40,5 €/month

0,22 €/kWh flat∗15 kWh/day∗30 days/month∗ 0,4 flat hour consumption/total consumption=39,6 €/month0,22 €/kWh flat∗15 kWh/day∗30 days/month∗ 0,4 flat hour consumption/total consumption=39,6 €/month

0,08 €/kWh valley∗15 kWh/day∗30 days/month∗ 0,3 flat hour consumption/total consumption=10,8 €/month0,08 €/kWh valley∗15 kWh/day∗30 days/month∗ 0,3 flat hour consumption/total consumption=10,8 €/month

Resulting in an energy consumption cost of 90,9 €/month meaning 23,4 €/month of savings which represents 20% of the total cost and a total of 280,8 €/year. 

The wholesale electricity market is the place where both generation and demand fit every day in Europe. Here, power plants come and sell the energy production each day to the customers who are willing to pay for this energy supply.  

It is important to know that energy has to be produced when it is used since it can’t be stored (this is changing nowadays with the emergence of batteries and other energy storage solutions). This adds a special complexity to the market since it means you must know the energy demand in advance in order to know how much electricity will be produced. 

Market Operation

The standard procedure is to gather offers from both generators and consumers in the one-day-ahead market, which negotiates the electricity to be sold and bought for each hour of the following day. This way, today, companies buy and sell the electricity to be consumed and produced each hour of tomorrow, tomorrow will negotiate the energy for the next day and so on. 

After this, generators and consumers may change their position by selling or buying extra energy in the intraday markets, taking place closer to the real-time delivery so their forecasts of the production and demand are more accurate. 

Price definition 

Regarding price formulation, focusing on the day-ahead market, first thing to do is to gather all generation offers and stack them together from the cheapest one to the most expensive one. Additionally, take all the buying offers and stack them from the most expensive (the buyer that is willing to pay more money to buy the electricity it demands) to the cheaper one (the one that is willing to pay less).  

Once this has been done, the day-ahead electricity spot price is determined for every hour as shown in Figure 4.

 Figure 4 

Regarding the dark curve, the first step represents one energy generation plant or technology offering a certain amount of energy at the price corresponding to the heigh in the graph. Additionally, a second energy generation technology places its offer in the second step and so on (typically, those steps with lower prices corresponds to wind, solar and other renewable sources or nuclear plants while those offers with higher prices are usually coal, gas turbines and other fossil fuels. 

Remember that these offers are made hourly, so for each day-ahead market we will have 24 curves like this one, one for each hour of the day. 

Each step of the blue (demand) curve is a buy offer from a consumer, while each step in the black (supply) curve represents a sell offer made by a generator. The point where the demand curve crosses the supply curve is called the marginal point. After this point, there are left only suppliers that are willing to generate electricity at high prices, while the remaining buyers only want to buy at low prices. 

For the rest of the offers, there is always one customer who is willing to pay enough money for the electricity to satisfy the supplier’s expectations. Hence, the offers at the right of the marginal point are rejected, while the ones on the left side of the graph are accepted. 

Nonetheless, those generators and/or consumers who did not make it to get into the auction can try another time in the intraday markets, where another auction takes place, and they may modify their offers of the day-ahead market. 

The spot price is the price at the intersection of both curves, and it determines the final price of the electricity at that hour of the day. Meaning that even if the first supplier were willing to generate for a lower price, he would receive the spot price for doing that. On the other hand, even if the first consumer would have to pay more money for the electricity, he will pay the spot price for it. 

How can DR help with the electricity price? 

Attending the previous points, one of the most interesting scenarios for the final users would be one with as low spot price as possible for each hour, due to the close relationship between this spot price and the dynamic price for those with tariffs directly linked to the wholesale electricity market, but also for will allow electricity suppliers to offer more attractive tariffs for their customers. 

Again, on this behalf, Demand response may serve as a helping hand since it would directly affect the formulation of spot price by changing the demand curve and thus shifting down the purchase orders made by electricity suppliers and industries. 

In the previous example, the interception between demand and supply curves occurs at a price of 42.9 €/MWh while adding Demand Response would push demand curve down, resulting in a hypothetical electricity spot price of 40 €/MWh. Furthermore, this decrease in demand curve and thus in the spot price would result in an additional environmental benefit since typically cheap energy generation is related to sources with lower carbon footprint than other conventional generation such as solar, wind, hydro or nuclear energy. Consequently, being able to shut down thermal energy while enhancing the use of other renewable energy sources and reducing load in hours of big stress for the energy grid are defined as cornerstones for the future of the electricity system. 

Go Deeper with spot price formulation and demand response effect 

The previous example might serve as an easy way to understand demand response effect on demand curve and thus on price definition, but as stated in the Price definition section, buy and sell offers are not linear but typically are distributed as a certain amount of energy offered at a certain buy/sell price from one or more agents, and ordered in steps. For a more realistic scenario, a new example is presented in this section. 

In the following graphs a more precise scenario is showed, where the spot Price is 65 €/MWh resulting from the intersection of the “sell/purchase orders” curves. 

In this example, every purchase order above 65 €/MWh and every sell order below 65 €/MWh are accepted. As appointed before, those cheap sell offers correspond typically to renewable energy sources (green area) and more expensive ones are usually those from fossil fuels, (combined cycle, coal, etc), so if we instead modify this previous example to simulate a day without high renewable generation (lack of wind, cloudy day, etc.) the previous example may turn into the one shown into the next figure. 

When lacking competitive offers from renewable sources in the market, other more expensive emerge to fill the gap. As a result in the previous example, spot price increases from the original 65 €/MWh to 75 €/MWh if demand stays rigid. 

If we analyse this previous scenario lacking renewable sources but increasing demand (simulating one hour of high demand for example near 20:00h with no sun in a day with poor wind generation) the spot price will be even more elevated, reaching 80 €/MWh in the example. 

One thing to notice from the previous example is that demand is as determinant as generation for the formulation of the spot price. The same way as the price goes up when demand grows up, it can also decrease if we implement demand response or other flexibility measures. To show up and starting from the previous situation with low renewable energy generation, this example can be modified to illustrate how would evolve the spot price if demand goes down. In the figure, the previous demand scenario is represented with dashed grey line, which results in the previous 75 €/MWh spot price. In blue is shown a modification of the previous example with lower demand because of a DR call for high price energy buyers. This new scenario results in a new spot price of 65 €/MWh, improving the performance of the spot price from end consumer’s perspective. 

As shown, demand response is such a powerful tool with enough potential to mitigate negative effects of including fossil fuels into the generation mix with tremendous benefits in economical but also environmental aspects. Additionally, demand response also has the ability to reduce grid stress during peak hours improving the electricity system’s performance and resilience. 

Generation Mixture 

As mentioned before, there are several different technologies to generate electricity from those that suffer from greenhouse gas emissions such as coal or combined cycle power plants (associated with penalties nowadays and more expensive to operate than renewable energy sources such as photovoltaic or onshore wind turbines). Each one of these technologies relies on its energy generation cost to compute the final price of the offer to send to the electricity market, so it’s easily understandable how the spot price would be strongly influenced by the technologies involved in the generation mixture each day. Avoiding technologies with high marginal costs, such as natural gas or coal, would be a watershed in the electricity market and the electricity price itself alongside with other methodologies such as demand response and demand flexibility. 

Since the discover of electricity, this sector is currently evolving to more efficient and reliable sources to provide and manage this form of energy. In the last years, renewable energy has abruptly irrupted in the electricity market becoming a predominant force in lots of markets, and a way to decarbonise the system while electrifying sectors typically powered by fossil fuels. Now, at the zenith of renewable energy deployment, energy and electricity sectors are struggling with new and old challenges in this new scenario: 

• One of the main challenges is the lack of control over the availability of renewable energy sources and strong shifts in the demand curve, which may lead to the necessity of turning on peak power plants powered by natural gas to fit the generation and demand curves. On this behalf, demand response can play an active role in mitigating this undesirable market behaviour while making the electricity system not only cheaper but also greener and more reliable. 

• The global shift towards sustainable energy sources has ushered in a new age for electricity markets worldwide. As nations strive to decarbonise their energy sectors, the proliferation of renewable energy technologies, such as wind and solar power, is leading to a paradigm shift in how electricity is generated, distributed, and consumed. However, this transition is not without its challenges. Among these is the integration of fluctuating renewable energy sources into the electricity grid, whilst simultaneously managing the evolving demand patterns that are a consequence of the electrification of transport and domestic heating. 

• The electrification of transport, through the widespread adoption of EVs, is anticipated to introduce significant new loads onto the grid. Similarly, the transition from fossil-fuel-based heating to electric heating systems is assumingly going to make peak demand periods more usual and worse, particularly during the colder months. These developments need a sophisticated approach to demand management for avoiding overloading the grid infrastructure and to ensure a stable and reliable supply of electricity. 

• A critical aspect of integrating renewable energy into the grid is the ability to store excess electricity during periods of low demand and high generation. However, the high costs associated with current electricity storage technologies, such as Li-ion batteries, limit their widespread adoption and pose a significant barrier to achieving supply-demand balance. This issue is worsened by the need for large-scale storage solutions to compensate for the intermittency of renewable energy sources. 

Demand response emerges as a pivotal strategy in the alignment of supply and demand within the electricity sector. By incentivizing consumers to adjust their consumption patterns, Demand response can mitigate the potential consequences on the grid during peak periods and provide a buffer against the variability of renewable energy generation. However, the effectiveness of Demand response strategies requires the development and implementation of advanced smart-metering infrastructure, real-time data analytics, and consumer engagement strategies.