**4. Market rules to incentive NCRE integration together with a safe system operation**

#### **4.1 Generation dispatch programming**

The determination of the hourly production of each generator that participates in the market results from an operation programming that covers different time intervals from 1 day, 1 week, and the medium/long term. This makes it possible to guarantee that the programming of the operation meets the objective of supplying the demand with adequate quality service at a minimum cost within the time horizon covered by each program [7].

In electrical systems with high participation of hydro generation in the generation mix, the energy that is stored in the reservoirs is used to supply the demand, thus avoiding fuel costs in the thermoelectric units. However, the availability of hydro energy is limited by the storage capacity in the reservoirs, which also affects the operational safety of the system.

This introduces a dependency between today's operating decision and future operating costs, including possible costs due to insufficient generation capacity resulting in demand cuts (non-supply energy).

If the existing hydro reserves are used in the short term to minimize thermal costs and a severe drought occurs in the future, high-cost rationing could occur, affecting the quality of service.

If, on the other hand, the hydro reserves are not used in the short term, through more intense use of thermal generation, and the future hydro inflows to reservoirs are high, water spill may occur, which represents a waste of energy and, consequently, an increase in the operating cost of the electrical system.

The optimal use of the water stored in the reservoirs corresponds to the point that minimizes the sum of the total costs incurred (present plus future costs). As shown in **Figure 6**, the hydro production that minimizes total cost is where the derivatives of the present cost (FCI) and future cost function (FCF), with respect to the volume of water stored in the reservoirs, are equal with opposite signs.

$$\frac{\partial \left(Total \,\mathrm{Cost} \right)}{\partial V} = \mathbf{0} \tag{2}$$

where

V [m3 ]: Stored volume of water in the reservoir. Then

$$\frac{\partial}{\partial \mathbf{V}} \mathbf{FCI} = -\frac{\partial}{\partial \mathbf{V}} \mathbf{FCF} = \mathbf{Water} \text{ Value} \tag{3}$$

The last equation says that minimum total cost is achieved when the reservoir reaches a level where the marginal immediate cost of using water is equal to the marginal future cost of using water now (with a different sign).

Any of both derivatives are known as WATER VALUE [\$/Hm<sup>3</sup> ] and represent the "opportunity variable cost" of the stored water.

Immediate cost function (FCI) is directly calculated as the least-cost thermal complement to hydro energy production. The future cost function (FCF) is conceptually calculated through the simulation of future system operation and the calculation of corresponding operating costs.

Due to the variability of inflows to reservoirs, which fluctuate seasonally, regionally, and from year to year, this simulation is carried out on a probabilistic basis, that is, using a large number of hydrological scenarios (historical data; dry, medium, and wet years).

SIMULATION = > FUTURE COST FUNCTION.

FUTURE COST FUNCTION = > HYDRO OPERATION POLICY FOR SHORT TERM.

The optimization of generation resources in a hydrothermal system, such as the ones existing in LATAM's countries, requires the use of mathematical optimization models that simulate the hierarchical decision-making process that must be carried out (strategy, tactics, and operation).

**Figure 6.** *Hydro production optimization.*

*Variable Renewable Energy: How the Energy Markets Rules Could Improve Electrical System… DOI: http://dx.doi.org/10.5772/intechopen.107062*

The generation dispatch is the result of sequential processes of calculation that divide the total problem, in time and space (multiples reservoirs), based on solvable models in reasonable computational times, which are coupled to each other through the conditions of the border that joins them [8].

The level of detail of the modeling, in time and space, is a compromise between the information required, the complexity of the mathematical problem, and the computational resources available.

Thus, planning the operation of hydrothermal systems can be divided into two major steps, which are as follows:


The optimization models related to both processes are quite well-known and are generally used in one way or another by electricity system operators around the world [9–11].

Given the differences between the LTP and STP models, each one of them is executed and solved independently (at different times) (**Figure 7**).

The short-term optimization is carried out using the boundary conditions established by the LTP model for the **TST** time, which determines the end of the period for which you want to schedule operations with the STP model, called shortterm scheduling that covers period {1, **TST**}; the LTP model covers the period {**TST** + 1, **T**} that needs to be analyzed to determine the future implications of decisions made in the present, {1, **TST**}.

Frontier conditions between both models can be established at least in one of the following ways:

• **Through economic variables (dual/prices):** Based on the setting, the opportunity price of the stocks at the end of period {1, TCP}, in this case, the hydro resource (water stored); it also applies to other storable resources such as the unused amounts of previously purchased fuel (take or pay contracts).

**Figure 7.** *STS and LTS models coordination.*

• **Through physical variables (primal/quantities):** Based on the setting, the amounts of resources available to be used during the operating period {1, TCP}.

Then, the minimum simulation period for the long-term programming is different for each system, according to their total water regulation capacity:

Brazil => 4 years Chile => 4 years Colombia => 2 years Argentina => 1 year Ecuador => Seasonal

#### **4.2 Energy marginal cost**

The generation economic dispatch results in the production of each power plant in each time interval (1 hour).

In each hour, the energy marginal cost is equal to the VPC of the generator with the highest VPC that, in each hour, is producing energy according to the results of the economic dispatch. The energy prices in the spot market are equal to the energy marginal cost [12].

**Figure 8** show historical energy prices for MX Market (Local Marginal Prices (LMPs) in the Day-Ahead Market (DAM)) at the Hermosillo node.

The marginal costs of energy have intraday and seasonal variations. The intraday variations, with a time step of 1 hour, are mainly due to (i) the hourly variations of the system demand (typically, the system demand is maximum in the evening/night hours and minimum demand in the early morning hours), (ii) the production of solar generation that reduces the energy prices at solar hours, and (iii) any time, by the effect in the power balance resulting from the forced outage of a generation unit or a transmission line.

Within the year, the average marginal costs of energy vary mainly due to the added effect of (i) seasonal variations in demand (in MX typically the demand in the summer months is greater than that of the winter months), (ii) due to the effect of

**Figure 8.** *Hourly LMPs at Hermosillo node (Y2021).*

*Variable Renewable Energy: How the Energy Markets Rules Could Improve Electrical System… DOI: http://dx.doi.org/10.5772/intechopen.107062*

rainfall that modifies the water inflows that reaches the reservoirs of the hydro plants, and (iii) seasonal changes in fuel prices.

If at some moment there is not enough generation availability to supply the demand, including the reserve margin for frequency regulation, the energy marginal cost result equal to the cost of not-supply energy (CNSE). This results in a strong economic signal toward improving the availability of generating units and thereby improving the security of the system.

The growing addition of new intermittent generation capacity (wind and solar) will produce variations in the generated power that can be significant in time intervals of less than 1 hour. In these operating conditions, for the marginal cost of energy to produce efficient economic signals to promote the availability of generation, it will possibly be necessary to determine the marginal costs of generation in time intervals of less than 1 hour [13, 14], mainly in markets with low hydro participation (like Mexico).

As a result, greater volatility is expected at energy prices in the spot market due to variations in the marginal costs of energy resulting from the intermittency in the production of NCRE generators.

To mitigate the risks of high prices in the spot market, supply contracts, with freely agreed conditions between generators and consumers, are efficient commercial instruments that allow:


**Note:** In LATAM's electricity markets, the supply contacts are **financial contracts** (not physical contracts). This means that the generator supplies, each hour, the contracted energy with its production, if it is dispatched, or by buying the contracted energy in the spot market if it is not dispatched, so financial contracts do not modify the economic generation dispatch.

#### **4.3 Day-ahead markets and intraday markets**

At the international level, there are several examples of wholesale energy markets with two or more settlement instances. An example is the wholesale electricity market of Mexico, where a day-ahead market (DAM) and a real-time market (RTM) operate. The other LATAM's markets are all markets that operate only in real-time.

In markets that operate only in real-time, the income of the generators in each hour (\$GEN(h)) results from valuating the energy generated (EG(h)) at the marginal cost of the hour (CMgR(h)).

$$\text{\\$GEN}(h) = \text{EGR}(h) \times \text{CMgR}(h) \tag{4}$$

In the markets that operate with two or more settlement instances, for example, MX where there is the DAM and the RTM, the income of the generators is determined by the following expression:

$$\text{\#GEN}(h) = \text{EGA}(h) \times \text{CMgA}(h) + [\text{EGR}(h) - \text{EGA}(h)] \times \text{CMgR}(h) \tag{5}$$

In this case, the income of the generators has two terms, which are as follows:


Therefore, in markets where there are at least two settlement instances (DAM and RTM), the generators are exposed to the uncertainty of the marginal costs in the RTM due to the difference between the expected energy to be generated (offer to the DAM) and the energy real generated in the RTM.

This exposure generates risks that generators seek to mitigate by improving their production forecasting tools for the following day. This helps to mitigate the adverse effects of the volatility of the production of NCRE generators on the security of supply. In markets with multiple intraday settlements (like European electricity markets), this is improved due to the probable production in real-time being better known.

In summarizing, the advantages of multiple settlements are: (i) allows generators to hedge the risks associated with real-time price volatility; (ii) reduces operating costs by allowing the market operation to be better scheduled to meet the demands at a minimum cost; and (iii) allows programming the operation of resources to deal with the uncertainty of large-scale variable renewable generation.

Given the existing structural differences between the different electricity markets (e.g., due to the generation mix that supplies the demand), the implementation of the multiple settlements scheme requires special attention to analyze the effect that these have on the generators' incentives to have high-precision forecasts, the opportunities for better coverage of risks due to price variability in the real-time market, and the incentives to install flexible generation resources that are capable of adjusting their dispatches at short notice. Other aspects to be evaluated are the number of settlements that it considers in the short-term market, the products that are traded in the multiple settlements (energy, ancillary services), and the sophistication of its matching models.

By way of background, it should be noted that markets in the United States are typically characterized by a day-ahead market and a real-time market (i.e., twosettlement markets), with co-optimization of energy offers and frequency reserves, and with matching algorithms from the previous day's market that consider unit commissioning restrictions and their associated costs, in addition to transmission system restrictions, based on a so-called Security Constrained Unit Commitment (SCUC). On the other hand, wholesale markets in Europe are characterized by the sequential acquisition of reserves and energy, where energy is traded in auctions the day before and multiple instances of intraday auction markets or continuous bilateral transactions [15]. In this case, the daily and intraday market instances do not consider security restrictions and transmission corridor congestion, which are evaluated and managed by each system operator, generating deviations in the real operation for market matching.

#### **4.4 Forecast of the NCRE generation production profile**

To schedule the market operation, the market operator needs to forecast the probable production of NCRE generators. NCRE generation forecasting is a rapidly evolving field [16, 17].

*Variable Renewable Energy: How the Energy Markets Rules Could Improve Electrical System… DOI: http://dx.doi.org/10.5772/intechopen.107062*

With the expected increase of NCRE generation, including generation within the distribution networks, the problem of forecasting the production is complicated since usually there is no information on the NCRE produced within the distribution networks. In these cases, the net demand of the distribution system must be forecast, which is equal to the consumed demand minus the existing NCRE generation in each distribution system.

To increase reliability in the operation of the market, regulatory changes should therefore be introduced to improve the quality of information on the expected production profile of NCRE generators. Improving the forecasts should be the combined responsibility of the system operator and the market agents. Departures between forecast and actual values should give rise to economic incentives to improve forecasts, for example, via intraday markets above described.

#### **4.5 System reserves**

The generation resources that provide flexibility to the electrical system are typically the following:


The development of the aforementioned technologies/schemes in the electricity market may result from the economic signals produced by the market. When this is not possible (lack of competition, oligopoly), regulated mechanisms (Ancillary Services) are created to make the development of the necessary technologies economically viable to achieve a safe and minimum-cost operation of the electrical system.

The amount of reserve (i.e., for frequency regulation) required in the system results from a trade-off between the cost of the reserve and the resulting quality of service.

The determination of the required reserve is carried out through reliability studies [18–20] that result in reliability indices (e.g., energy not supply) associated with insufficient generation for different values of power reserve. Based on the results, the cost of the reserve and the cost of the quality of service are determined. The optimal reserve is the one that allows minimizing the sum of the costs of the reserve plus the costs of the NSE due to insufficient generation.

#### **4.6 Optimal reserve power determination**

The optimal reserve power required by the electrical system is determined through reliability studies that allow knowing the quality of the supply of the demand based on the reserve power in the electrical system.

For example, the quality index could be the ratio between the NSE and the total energy supplied, or the probability of loss of load, both due to the effect of forced contingencies in the generation fleet. In what follows, these quality indices are identified as RE (**RE**liability Index).

Electrical systems are composed of a large number of generating units, each with its own technical and availability characteristics, including the availability of primary resources in the case of renewable power plants (inflow water, wind speed, and solar radiation levels), and where demand varies from time to time following generally known and repetitive patterns. In these kinds of systems, and considering a very low allowed NSE (in the range of 1 � <sup>10</sup>�<sup>3</sup> to 1 � <sup>10</sup>�<sup>5</sup> of the served energy), the system quality index RE is typically an exponential function of reserve power (PRES) required to control the quality of the supply. The NSE is proportional to RE index.

$$RE \equiv e^{-k \times PRES} \tag{6}$$

$$\text{NSE} = k\_1 e^{-k \times \text{PRES}} \tag{7}$$

where

k, k1: Constant that results from system reliability studies.

PRES [MW]. System reserve power.

The total cost incurred in the electrical system results from the sum of the cost of the energy not served that results in a certain quality index RE, plus the cost of providing the reserve power that allows obtaining said quality level.

$$\text{Total Cost} \left[ \text{s} \right] = \text{CNSE} \left[ \frac{\text{s}}{\text{MWh}} \right] \times \text{NSE} [\text{MWh}] + \text{CRES} \left[ \frac{\text{s}}{\text{MW}} \right] \times \text{PRES} [\text{MW}] \tag{8}$$

where

CNSE: Unitary cost of the NSE. NSE: Not supply energy resulting from reliability analysis. CRES: Unitary cost of the reserve power. PRES: Reserve power. In the optimum

$$\frac{d\ (Total\ Cost)}{d\text{PRES}} = 0.0\tag{9}$$

That allows to obtain the optimum value of System Reserve Power (PRESopt):

$$\frac{d\ (\text{NSE})}{d\text{PRES}} = a = -\frac{\text{CRES}}{\text{CNSE}}\tag{10}$$

$$PRES\_{opt} = \frac{\ln\left(k k\_1 \text{CNSE}/\text{CRES}\right)}{k} \tag{11}$$

**Figure 9** shows the described optimization process. The red curve shows the variation of the non-supply energy (NSE) as a function of the reserve power (PRES). The higher the reserve, the lower the NSE. The optimal PRES is the one that meets the condition that the derivative of the NSE, with respect to PRES, is equal to alpha (α).

*Variable Renewable Energy: How the Energy Markets Rules Could Improve Electrical System… DOI: http://dx.doi.org/10.5772/intechopen.107062*

**Figure 9.** *Optimal power reserve.*

#### **4.7 The cost of non-supply energy (CNSE)**

As demonstrated in the previous point, the reserve power (PRES) required by the electrical system depends on the cost of not supply energy (CNSE), with the reserve power being greater the higher the CNSE.

The CNSE concept includes a group of economic costs that can affect society, as a whole, when the supply of electricity cannot be provided to the extent required by consumers. The NSE is the amount of energy potentially demanded (presumed energy) that cannot be supplied.

In the commodity markets, in the absence of enough supply, the price of the product increases and the quantity demanded adjusts automatically (elasticity), first withdrawing those consumers with lower utility or consumer surplus, which is economically efficient, thus minimizing the reduction in societal benefit.

However, the electricity sector has certain special characteristics, due to which the CNSE concept is used, instead of the more natural idea of balance between supply and demand:


The valuation that consumers make of the NSE, in general, needs to be estimated. The economic costs that can affect society as a whole when electricity supply is not enough are of various kinds. The main difficulties that arise in estimating the CNSE are:


There is a wide variety of methods that can be used to calculate the CNSE. There are two large families of approaches, which are as follows:


The CNSE is significantly reduced if the interruption condition can be anticipated by the consumer in such a way that they can take precautions in the event of a probable supply interruption. To take this aspect into account, the CNSE is determined for short-term failures (when the failure condition is not prevented) and longterm failures (when the failure is scheduled and known by the consumers).

## **4.8 Capacity balance market**

The purpose of the **Capacity Market** is to provide an economic signal to incentivize the installation of enough generation capacity to supply demand while satisfying defined reliability criteria [22].

If the generation fleet was designed and operated to supply the system demand at a minimum total cost (the sum of investment costs (CAPEX) plus OPEX operating costs), it is fulfilled that all generators cover all of their investment costs and operation (sufficiency principle) if all generators receive as remuneration: (i) a payment for energy (\$ENE), which results from valuing their production at the Marginal Cost of the Energy in the market, and (ii) payment for firm capacity (\$POT), which results from valuing their firm capacity at the price of capacity (PPOT) in the power balance market.

The value of PPOT is determined in most of LATAM's markets as the annual fixed costs (CAPEX, fixed O&M costs) of a Turbo Gas-type power plant with an installed capacity approximately equal to the annual growth of the maximum demand of the system.

The payment for capacity that generators receive has a direct effect on the reliability of the electrical system.

With capacity payments, generators require lower marginal rent (spark spread) to cover their capital costs (CAPEX). So, for the same demand, lower marginal rent results from lower market marginal costs that result from a higher generation availability and consequently lower non-supply energy (NSE) probability.

*Variable Renewable Energy: How the Energy Markets Rules Could Improve Electrical System… DOI: http://dx.doi.org/10.5772/intechopen.107062*

The firm capacity of the generating units depends on their technology. For hydroelectric plants, firm capacity is determined typically as the power generated in the hours of maximum demand (or minimum reserve) for a very dry hydrological condition.

For thermal power plants, firm capacity is usually equal to their average available capacity. Large thermal power plants, compared to the supplied demand, produce large disturbances in the operation of the electricity market when they go out of operation due to an unscheduled event (failure). When the power of the failed plant exceeds the reserve margin for frequency regulation, a load cut will be necessary to balance supply/demand, a situation that could lead to a massive load cut. In such a situation, the firm capacity of large thermal plants could be reduced as an economic signal that shows the impact on the quality of service of the system.

As an example, **Table 3** shows the installed capacity and forced output rate (FOR) of a fictitious generation fleet made up of 10 generating units (G#1 to G#10). One of the generators (G#3) has an installed capacity (300 MW) equivalent to 25% of the total installed capacity of the generation park.

**Figure 10** shows the total power available for a given probability of exceedance resulting from considering all the possible operating states (210) of the generation park considering the operating states of each generation unit.


#### **Table 3.**

*Example of the generation fleet.*

**Figure 10.** *The firm capacity of thermal plant.*

**Figure 11.** *Effective load carrying capability of NCRE.*

Two curves are presented, the blue curve considering the entire generation park and the red curve WITHOUT generator G#3.

It is observed that for a probability of exceedance of 95%, generator G#3 contributes only 150 MW, while its average available power is 240 MW, so the firm capacity of generator G#3 should be reduced to 150 MW.

Up to now, in the LATAM's markets, there is no consensus on the firm capacity of NCRE plants. The main reason is that it cannot be guaranteed that this type of generator will produce energy during the hours of minimum reserve of the system, which are typically at night hours.

In markets with a significant share of hydro generation in the generation mix that supplies demand, such as most of the LATAM's markets, it is observed through reliability studies that with NCRE generation, a greater demand can be supplied without compromising the quality of service. This is justified because the production of NCRE generation allows a greater volume of water to be stored in the reservoirs, which allows a greater hydro generation in hours of minimum reserve.

As an example, **Figure 11** shows the evolution of a reliability index for different levels of demand in the Peruvian market. Two curves are presented, WITH and WITHOUT NCRE generation (installed capacity 1100 MW). It is observed that WITH NCRE generation, demand can be increased by 750 MW without reducing the reliability index, which shows that in the Peruvian system, NCRE generation effectively contributes to improving the quality of service in supplying the demand [23–25].

In electrical systems with high participation of hydro energy, the hours where there is a greater risk of not being able to supply the demand is at evening/night hours when the demand is usually maximum. In these systems, the firm capacity result from the generation available in this hour range.

On the other hand, in systems with low participation of hydro generation, the supply risk is at any time of the day and even more so with significant participation of NCRE generation in the generation mix, as occurs in the MX electrical system [26].

For this reason, in the MX electrical system, the firm capacity of an NCRE generator results from the generator production in the so-called critical hours, the 100 hours of minimum reserve of the year. **Figure 12** shows the system generation reserve, in MX, in each hour of 2019, in red the 100 critical hours. The firm capacity of the NCRE generators is measured in these critical hours.

*Variable Renewable Energy: How the Energy Markets Rules Could Improve Electrical System… DOI: http://dx.doi.org/10.5772/intechopen.107062*

**Figure 12.** *MX capacity balancing market – 100 critical hours, 2019 year.*

#### **4.9 Storage energy systems**

Storage energy systems are a set of technologies and operating methods that allow energy to be conserved for later use (similar to what happens with hydro plants). Energy storage is currently based on a broad set of technologies, many of which already have a solid state of maturity, while others are less consolidated, which require progress in some aspects and improve their performance, costs, and competitiveness.

Examples of existing storage systems in LATAM's countries are (i) batteries (BESS), in which the charging phase is carried out by storing chemical energy in the batteries, and (ii) pumping hydro plants in which during the charging phase it accumulates water in the reservoir (potential energy) by pumping, which is transformed into electrical energy during the discharge phase. The charging phase is usually fed with energy withdrawn from the same network into which the accumulated energy will be injected.

Of the storage technologies, it is worth highlighting the high potential for the development of BESS, which constitutes an effective complement to NCRE generation for the safe supply of demand at a minimum cost. Other possible uses of BESS concerning the safety and quality of the energy supply are as follows:


The NCRE generation technically today can offer operating reserves for frequency regulation. However, it must be taken into account that since NCRE generation has zero production opportunity cost (the energy that is not produced is lost) by requiring a power reserve margin to offer the regulating band, the provision of reserves normally has a high operating cost results from the difference between the price of spot energy and the variable cost of the generator, which is zero. This margin is usually much lower in other technologies, so from the economic point of view, it is better to assign the frequency regulation reserve to conventional power plants or storage systems.

Storage generators, particularly batteries, can be a lower-cost alternative for NCRE generators to provide power reserves for frequency regulation.

NCRE generators can also increase their firm capacity by adding batteries to their installed generation capacity, charging the batteries with their generation at hours when there is a high reserve margin (low marginal energy prices), and injecting the stored energy into the electrical system at hours of the day when the reserve is minimal (high marginal prices). This configuration is called a "hybrid generator."

The storage generators can also operate in the electricity markets independently of other generators in the system, under the so-called "stand-alone" configuration. In this case, the owner of the storage media (battery) covers the investment costs via the marginal rent that results from the storage process in hours of low marginal cost and the sale of the energy stored in hours of high marginal cost. The difference between the marginal costs during loading and unloading of the storage medium must cover at least the losses in the process of loading/unloading the storage medium.

A stand-alone storage generator can also be remunerated by its firm capacity. In this case, system operation studies must demonstrate that it will be economically convenient to charge the storage system when the electrical system is in a critical situation with a minimum reserve margin. The economic feasibility will depend on the difference between the marginal costs between the loading and unloading processes when the electrical system has a minimum reserve. If the difference in marginal cost does not cover the cost of losses in the storage system, so the firm capacity will be equal to zero.

#### **4.10 Demand participation**

The demand can participate directly in the spot market via offers to withdraw demand if the price of energy in the spot market exceeds the offered price. To this effect, loads should have the technical capacity to respond to the ISO instructions to reduce the load in real time. From the point of view of the economical minimum cost dispatch, the demand withdrawal offers are similar to a thermal plant with negative capacity and a VPC equal to the offered price.

Consumers can also play an active role in the system's security of supply via their response to the electricity rates paid to the distribution company from which they buy energy. In many cases, the rates include high charges if the time in which the consumer has his maximum demand coincides with the time of day in which the aggregate demand of the distribution company is maximum. In these cases, the consumers, mainly industries, reduce their consumption, so, their demand is elastic to electricity rates. This can be seen in **Figure 13**, which shows the hourly demand of a typical day for large consumers in the Peruvian market. In the hours where rates are high, 6– 10 pm, demand is significantly reduced. This lower demand, at times when the system *Variable Renewable Energy: How the Energy Markets Rules Could Improve Electrical System… DOI: http://dx.doi.org/10.5772/intechopen.107062*

**Figure 13.**

*Peruvian electricity market. Hourly demand of large consumers.*

typically has minimal reserves, contributes to increasing system reserves and thus improving the security of supply.

## **5. Conclusions**

In the 1990s, wholesale electricity markets were organized in the LATAM's countries to introduce competition in the supply of electricity as a way of supplying demand with an adequate quality of service at a minimum cost.

The initial design of these markets mainly considered that the demand would be supplied with a mix of hydro and thermal generation.

In recent years, the growing participation of NCRE energies, mainly wind and solar-PV, in the generation mix has been observed, promoted by a significant reduction in the capital costs of these technologies and the need for countries to advance in the replacement of thermal generation, mainly the generator based on coal, to mitigate the effects of climate change and thus move toward a production matrix-based mainly based on clean energy (energy transition).

The greater participation of solar and wind energy, characterized by variable generation depending on climatic conditions (wind level and solar radiation), introduced a strong instability in the electrical systems that put at risk the normal supply of demand in safe conditions.

To mitigate these effects, the rules of the wholesale electricity markets of the LATAM's countries include, or their implementation is being evaluated soon, strong economic incentives that seek to maximize the availability of energy at times of minimum generation reserve. In this sense, the following stand out:


The electricity markets of LATAM's countries showed strong dynamism from the beginning, mainly due to private participation in the electricity generation segment.

It is estimated that this behavior will continue in the future, for which it will be necessary for market regulations to evolve to allow the integration of new technologies efficiently, thus contributing to achieving the objectives of the energy transition without compromising the secure supply of demand.
