Open AccessArticle

Electrochemical Storage and Flexibility in Transfer Capacities: Strategies and Uses for Vulnerable Power Grids

Gustavo Adolfo Gómez-Ramírez

^1,*

Luis García-Santander

José Rodrigo Rojas-Morales

Markel Lazkano-Zubiaga

⁴

and

Carlos Meza

⁵

Escuela de Ingeniería Electromecánica, Instituto Tecnológico de Costa Rica, Cartago 159-7050, Costa Rica

Departamento de Ingeniería Eléctrica, Universidad de Concepción, Concepción 4030000, Chile

Sede Regional Chorotega, Campus Liberia, Universidad Nacional, Liberia 50101, Costa Rica

⁴

Departamento de Tecnologia Electrónica, Universidad del Pais Vasco—Euskal Herriko Unibertsitatea, 20500 Eibar, Spain

⁵

Department of Electrical, Mechanical and Industrial Engineering, Anhalt University of Applied Sciences, 06366 Köthen, Germany

Author to whom correspondence should be addressed.

Energies 2024, 17(23), 5878; https://doi.org/10.3390/en17235878

Submission received: 11 October 2024 / Revised: 30 October 2024 / Accepted: 8 November 2024 / Published: 23 November 2024

(This article belongs to the Special Issue Challenges and Opportunities for Renewable Energy)

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Browse Figures

Figure 1
Methodological framework for improving the flexibility of transfer capabilities among various areas. "> Figure 2
Interconnection voltage behaviour without electrochemical storage. "> Figure 3
Interconnection power behaviour without electrochemical storage. "> Figure 4
Seven states sequence of the collapse explained in case study. "> Figure 5
Interconnection frequency behaviour without electrochemical storage. "> Figure 6
Siting and sizing for electrochemical storage in Central American power system according to <a href="#energies-17-05878-t001" class="html-table">Table 1</a>. "> Figure 7
Interconnection frequency behaviour with electrochemical storage. "> Figure 8
Interconnection power behaviour with electrochemical storage. "> Figure 9
Sequence of power system states shown in case study and proposed solution. ">

Versions Notes

Abstract

The integration of renewable energy sources into electrical power systems presents enormous challenges in technical terms, especially with energy storage. Battery electrochemical storage systems (BESSs) are becoming a crucial solution for reducing the intermittency of renewable energy supply and enhance the stability of power networks. Nonetheless, its extensive implementation confronts constraints, including expense, life expectancy, and energy efficiency. Simultaneously, these technologies present prospects for improved energy management, increase the hosting capacity of renewable energy, and diminish reliance on fossil fuels. This paper investigates the obstacles of integrating electrochemical storage into electrical power systems, explores solutions to use its promise for creating more resilient and sustainable grids, and presents a method for the size estimation and strategic allocation of electrochemical energy storage systems (EESSs). The aim is to improve grid voltage profiles, manage demand response, increase the adoption of renewable energy resources, enhance power transfer among various areas, and subsequently improve the stability of a power system during large disturbances. The methodology utilizes a multi-stage optimization process based on economic considerations supported by dynamic simulation. This methodology was tested employing a validated dynamic model of the Interconnected Electrical System of the Central American Countries (SIEPAC). The system experienced multiple significant blackouts in recent years, primarily due to the increasing amount of renewable energy generation without adequate inertial support and limited power transfer capabilities among countries. Based on the results of using the technique, EESSs can effectively lower the risk of instability caused by an imbalance between power generation and demand during extreme situations, as seen in past event reports. Based on economical constraints, it has been determined that the cost of installing EESSs for the SIEPAC, which amounts to 1200 MWh/200 MW, is 140.91 USD/MWh.

Keywords:

energy storage systems; hosting capacity enhancement; load management; power system planning; solar photovoltaic; wind energy; hydropower; renewable energy sources; capacity expansion planning; electrical grid

1. Introduction

Storing significant amounts of renewable energy requires power systems today to show more flexibility and adherence to both existing and emerging economic, technological, and regulatory constraints [1,2]. There is an overall tendency toward the utilization of distributed resources and the electrification of sectors such as transportation, which results in an increase in the demand for electricity and a decrease in the employment of traditional generation technologies such as hydro and thermal, which provide inertia. The variability and intermittent features of solar and wind power might result in unexpected imbalances between power supply and demand, hence heightening the risk of instability.

Strategic planning for transmission and distribution systems, in addition to the incorporation of intermittent generation, is essential for the future; nevertheless, it must be supported by battery energy storage systems (BESSs). The integration of considerable renewable energy requires current electricity systems to exhibit more flexibility and adapt to prevailing and forthcoming economic, technological, and regulatory limitations. A prominent trend is the adoption of distributed energy sources and the electrification of sectors like transportation, leading to heightened power consumption and a reduction in the utilization of conventional generation technologies, such as hydro and thermal, which offer inertia [1,2]. The variable and intermittent nature of solar and wind energy can result in unforeseen discrepancies between energy supply and demand, increasing the risk of instability. This has necessitated planning to explore alternatives for enhancing the electricity system to achieve increased resilience and flexibility under specific conditions while adhering to decarbonization policies [3].

The authors of [4] added BESSs to the new definitions and classification of stability phenomena in power systems to show how storage devices have evolved and how important they are becoming in today’s power system. This indicates the integration of energy storage as a relevant component in the development and expansion of power systems [5,6].

For example, BESSs may successfully manage power system oscillations and improve stability during disturbances. They offer numerous advantages, including the ability to be placed in multiple locations, which is beneficial as traditional methods such as hydro dams face increasing limitations, as emphasized in [7]. The limitation on power transmission capabilities at critical power lines [5,8] is one issue that these novel storage systems can also address.

The literature, as demonstrated in [9], indicates that the hosting capacity of distributed resources can be optimized through maximizing storage utilization. However, it is essential that the storage is appropriately scaled and incorporated into the transmission and distribution networks. In order to achieve maximal system resilience and reliability, it is important to develop strategies for sizing, allocation, management, and dispatch using storage facilities.

These strategies should take into account technical and economic limitations to minimize expenses and guarantee the intended utilization. Applications as demand responses necessitate transient stability [10,11,12,13]. Additionally, the authors of [14,15] suggest criteria considering numerous operating scenarios to ensure optimal performance. Hence, the process of determining the most efficient dimensions, location, and utilization strategy for an EESS requires simultaneous consideration of both planning and operational aspects. However, current research frequently focuses on single aspects of the problem rather than addressing it as a whole.

This paper presents a new approach for incorporating storage systems into vulnerable power grids that have weak links among areas that are susceptible to oscillations. The approach involves a series of dynamic simulations followed by a multi-stage optimization process. This optimization process aims to minimize investment costs and maximize the utilization of all available energy resources. This strategy seeks to consider both aspects of planning (sizing, allocation, and dispatch) and operation (dispatch and disturbance rejection), with the ultimate objective of optimizing the expenses associated with investments.

The methodology was implemented in the SIEPAC, which includes Guatemala, Honduras, El Salvador, Nicaragua, Costa Rica, and Panama. The system under evaluation successfully replicated real blackout events, and the algorithm determined that the storage devices minimized the risk of power generation and load imbalances. Additionally, the devices provided virtual inertia support, which prevented load shedding and blackouts.

The remaining part of the paper is structured in the following order: Section 2 describes the methodology used to improve power transfer capabilities in power systems that are vulnerable to instabilities caused by low-power transfer capabilities. It provides a comprehensive explanation of the proposed approach to address the problem using EESSs. Section 3 then demonstrates the implementation of the proposed approach in the SIEPAC model. Section 5 concludes this article.

2. Methodology

The methodology to size, allocate, and operate EESSs in this paper is explained in this section. A flowchart summarizing the methodology is shown in Figure 1. It is an iteration of stages involving optimization and dynamic simulations to incorporate both planning and operational criteria. The most important aspects are described as follows.

2.1. Model Data Input

The basic data required when implementing models and running simulations in power systems include demand and generation profiles, in addition to data on elements such as electric generators, transmission lines, reactive power compensations, loads, and power transformers. Nodal prices should be included here, as well as the topology of the grid, maximum transfer capabilities, frequency control thresholds, and so on.

2.2. Allocate the Best Candidate Buses

The first step is to create a composite index that allows us to rank the most attractive bus candidates to install part of the BESS capacity. The composite index described by (1) takes into account a number of factors, including nodal prices as well as electric and physical distances to interconnections.

S_{h o} (n) = X_{1} (n) + X_{2} (n) + X_{3} (n) + X_{4} (n) + \dots X_{s} (n) \forall \in N

(1)

In (1), the coefficients, designated as

X_{s} (n)

, have been defined as criteria for determining the allocation. Expression (1) is calculated for every n bus in the network during the analysis. The four coefficients

X_{1} (n)

X_{2} (n)

X_{3} (n)

, and

X_{4} (n)

are used to obtain a general coefficient,

S_{h o}

, which is a unitless and normalized value between 0 and 1. A higher

S_{h o}

value suggests the existence of favorable conditions for establishing a connection at one specific bus.

These coefficients are defined as follows:

$X_{1} (n)$ : it represents the weighting factor attributed to the nodal price at the bus, relative to the average of nodal prices in the studied area. Nodal prices are significant due to networks where costs impact the installation at the interconnection nodes; this consideration must be evaluated. Nodal prices can be established by an analysis of costs at the interconnection nodes or determined based on the information available from electric utilities. A bigger coefficient suggests that the node is less expensive.
$X_{2} (n)$ : it describes the physical or electrical distance to an interconnection node. The transfer of electrical energy necessitates a well-functioning infrastructure, regardless of the distances involved. However, economic limitations on investments make this feature extremely crucial. A value of 1 suggests that the node is in immediate proximity to the interconnection or area characterized by a higher requirement for energy, whereas a value of 0 signifies the opposite.
$X_{3} (n)$ : this weight factor takes into consideration system losses. If there have been substantial losses in the system despite the distances, it can pose a risk to operational safety and limit power flows. The quotient can be determined by dividing the losses of a line between interconnection nodes by the total losses in each area. The lower the losses of the line, the more attractive its buses.
$X_{4} (n)$ : it evaluates the proportion between the power limitations in an interconnection and the original power rated capacity of the line when it was built. As the limitations of power transfers between areas, these weight factor aim to encourage better performance in power systems and the location of installations in more convenient places, both economically and technically.

The methodological approach is flexible and can take into account multiple weight factors for each given value of

X_{s} (n)

2.3. Assign the MW and MWh EESS at Each Candidate Bus

EESS capacity designation (sizing) must take into account the potential for a generator, load, or any other power system component to fail under single-contingency scenarios (

k - 1

) and that such EESSs should be able to operate at rated capacity for a given period of time. This size study employed an optimization analysis. The following variables are involved in the formulation of the optimization problem:

$D_{m a x}$ is the overall demand of the entire system in $M$ $W$ .
$G_{i}$ : for each area i, the generators with the greatest rated capacity in $M$ $V$ $A$ . The loss of high-capacity generation raises the likelihood of an imbalance between load and generation.
$L_{i}$ : loads for every area i, with the biggest demand, measured in $M$ $W$ . The effect of this variable is analog to the previous point as it is fundamental in the energy balance and stability.

Following the identification of the highest possible

G_{i}

and

L_{i}

, the subsequent coefficients are determined:

$α_{i}$ = $G_{i} / D_{m a x}$ and $β_{i}$ = $L_{i} / G_{m a x}$ . The maximum and minimum values of $α_{i}$ and $β_{i}$ , respectively, indicate the risks associated with imbalance in the power system.
The coefficients $α_{i}$ and $β_{i}$ are used to calculate the Sizing Objective Function ( $O F_{s i z i n g}$ ) for the EESS in (2).

$O F_{s i z i n g} = \sum_{n = 1}^{b u s e s} P_{1} (n) Y_{1} + P_{2} (n) Y_{2} + \dots + P_{n} (n) Y_{n} \forall \in N$

(2)

The

P_{n}

coefficient represents the potential for a generator, load, or any other power system component to fail under single-contingency scenarios (

k - 1

), and it is calculated for every n bus in the network during the analysis. In the optimization function, the values of

α_{i}

and

β_{i}

correspond to the constraints, since they indicate the risks associated with imbalances in the power system. The range indicates the maximum and minimum values for which the storage requirements have to be sized.

The value of

Y_{n}

is determined by the system’s losses and corresponds to the desired placement of storage in places with optimal power transmission conditions. Enhanced efficiency is sought within the system; thus, taking into account the losses associated with this coefficient is crucial, as greater effectiveness of the EESS is required.

2.4. Operate the EESS Economically to Obtain the Setpoints

Developing the methodology requires calculating the following types of costs [5,16,17]:

Identify the Cost of Unit Installation ( $C_{i n s t}$ ): given in $\frac{U S D}{M W h}$ , this is required for calculating the following costs: per conversion system ( $C_{S - C}$ ), power balance ( $C_{B - P}$ ), operation and maintenance ( $C_{O & M}$ ), capital ( $C_{C}$ ), and construction and commissioning ( $C_{C & P - M}$ ). In order to achieve this, its necessary to obtain the following values.
Power Transfer Unit Cost ( $C_{p t c}$ ): in order to calculate the power transfer unit cost given in $\frac{U S D}{M W h}$ , first determine the nodal prices ( $X_{a}$ ) between the connecting locations. The cost has been attributed to the transmission of electrical energy through the power lines.
Determine the Cost Per Unit of Losses ( $C_{l o s s} (n)$ ): this is the cost per unit of losses that result from power transfer in the transmission lines.
As shown in (3), the Total Cost ( $C_{t o t}$ ) is expressed in USD per hour and can be calculated by summing all of the previously established costs.

$C_{t o t} (n) = \sum_{n = 1}^{b u s e s} C_{i n s t} (n) + C_{p t c} (n) + C_{l o s s} (n)$

(3)

Performing an economic analysis is required to minimize the power dispatch and the Unit Generation Costs. The Dispatch Objective Function (

O F_{d i s p}

) will be defined in (4) using the previously developed components, where

P_{d i s p}

represents the scenario power to be dispatched at the minimal operational cost.

O F_{d i s p} = \sum_{n = 1}^{b u s e s} C_{t o t} (n) P_{d i s p} (n)

(4)

Another index that is output by this second optimization is the Unit Generation Cost (

U G C

), which is given in

\frac{U S D}{M W h}

and can be estimated by dividing the total cost value

C_{t o t} (n)

by the rated power

P_{n o m} (n)

, as indicated in (5).

U G C (n) = \frac{C_{t o t} (n)}{P_{n o m} (n)}

(5)

2.5. Simulation and Status Evaluation

The results provide information identifying the most effective utilization of the EESS. The information obtained must be processed with the aim of determining the parameters of operation and power flows, guaranteeing that they remain within the defined range of values. Similarly, it is necessary to examine the analysis of dynamic stability in order to determine the consistency of the results obtained within every area and throughout the interconnections. Once the voltage, power flows, frequency, and stability conditions have been verified and found to be sufficient and satisfactory, the analysis concludes its cycle. Otherwise, the size of the EESS is increased and the process is iterated again until all constraints are met.

2.6. Improving Flexibility

The approach employed for selecting and sizing the EESS enables power dispatch in contingency situations. It is possible to maintain the balancing between demand and generation, as EESSs can provide power to the system in the event of a generating loss, while energy can be controlled for consumption during a demand loss. This allows for the management of power flows among the interconnections of electrical systems or between multiple locations within a system. Optimal dispatch provides appropriate energy management at minimal cost. This approach not only facilitates flexibility among interconnections but also enables service stations to offer auxiliary services, such as reactive power delivery, while maintaining ideal voltage profiles in the event of a minor disturbance. This component additionally enhances voltage flexibility locally at spots in the system where reactive power support is limited.

2.7. Constraints

This model may be constrained by inadequate information for creating expressions (1)–(3). Essential elements for developing an appropriate model, dependent on the particular situation, include nodal prices, network infrastructure data such as transmission line information, operational data that include system losses and limitations in power transfers among interconnections, and demand, generation, and consumption statistics, among others. A major challenge typically arises from economic and financial data, as this information is required for implementing economic dispatch. Occasionally, the cost data are severely constrained, or their inconsistency is a limitation, as they necessitate prior analysis and processing to yield acceptable results. The methodological approach, although flexible in including additional characteristics, may also consider additional factors that could carry a greater weight for the evaluation of the system in issue, depending on the particular situation.

3. Case Study

The case study examines the Central America Power System, which occupies an area of 522,760

{km}^{2}

and supports an estimated population of 50,690,000 people. The electric grid encompasses nations such as Guatemala, El Salvador, Honduras, Nicaragua, Costa Rica, and Panama, constituting a component of the Regional Electricity Market (MER) in partnership with Mexico. The transmission system operates through various voltage levels, with the connection between Guatemala and Mexico having a voltage of 400

k

V

. The countries in the region have been linked by a regional transmission line that consists of 18 power substations operating at a voltage of 230

k

V

The total capacity designed for this transmission line is 300

M

W

. The voltage levels used are 230, 138, 115, 69, and

34.5

k

V

, which were modeled and simulated using ETAP^®. A simulation was performed with 2192 buses, 650 generators, 852 loads, 137 static power reactive compensators, 2272 transmission lines, and 1210 power transformers. The simulation was based on a regional demand of

9080.52

M

W

. The EESS was considered with a virtual inertial of H = 6 s. The system parameters are not available as a consequence of confidential agreements. The methods were applied in order to mitigate the impacts of a significant failure in the previously identified electrical system, which will be explained in the following. The event occurred on 9 June 2021, when a disconnection took place on the transmission line connecting the Pavones and Santa Lucía Power Substations in Honduras’ Electrical Power System. As a consequence, 169

M

W

of PV generation was lost, resulting in an imbalance between power generation and demand.

The power imbalance in the interconnection between Mexico and the region contributed to an increase in power flow. This imbalance resulted in the tripping of the power breaker interconnection when the voltage drops below 0.97 pu and the power exceeds 300

M

W

for a timing longer than 11 cycles, as depicted in Figure 2 and Figure 3. A detailed model has been developed for the 9 June event and a solution was suggested from the perspective of the power system. The proposed approach evaluates the dynamic stability of the system as well as the behavior of voltage and frequency. The model uses optimization techniques to determine how to place, size, and cost-effectively send electrochemical energy into the Central American grid. This takes place in order to avoid system collapse during periods that involve significant disturbances.

Protection was configured to activate and open the power breaker interconnection when the following circumstances happen simultaneously: voltage (V) < 0.97 pu and power (P) > 300

M

W

. This situation represents a potential hazard due to its tendency to induce instability under specific circumstances of operation, as shown in the investigated case. It was determined that this situation had happened before.

In the simulation, the Mexican Power System transmitted 240

M

W

of power to the region. Figure 4 shows the power system’s status as it progresses during the events of 9 June. The blue line represents the progression of this phenomenon, and it can be seen that at the exact moment of failure, the voltage decreases to a value below 0.97 pu while transitioning from state 1 to state 2. This condition places the power system in a state of alert. As the power between the interconnection of Mexico and the region grew, the low voltage protection was triggered when the power exceeded 300

M

W

Figure 5 shows the frequency’s behavior during the failure, revealing that it dropped below

58.7

Hz

. This drop frequency occurred in stages 3, 4, and 5, as shown in Figure 4, and resulted in the simultaneous disconnection of numerous system loads. Currently, the power system is experiencing an emergency situation where the protection system is activated when it identifies an imbalance between the amount of power generated and the quantity of power being demanded.

The protection system’s main objective is to reestablish the balance between generation and demand with the objective to stabilize the frequency. As a result, there are significant increases in the flow of power among the interconnections of countries, resulting in the opening of interconnection power breakers. As a result, the interconnections between Honduras and Nicaragua and between Costa Rica and Nicaragua were activated, resulting in a total blackout in Nicaragua.

This caused the regional electrical system to split into two distinct sections: Guatemala–Honduras–El Salvador and Costa Rica–Panama, as shown in stage 6 when the system collapsed. Considering that the 230

k

V

buses are connected to the largest loads, the model will be implemented at this voltage level. With the losses seen in the regional system, which account for approximately 17.5% of the total, it is important to employ the solution methodology in the highest voltage system to mitigate the impact of these losses while improving the decision-making process. A total of 175 potential 230

k

V

buses have been placed under evaluation for the integration of storage.

4. Results

4.1. Allocation Determination and Sizing Optimization

The siting review indicates that Costa Rica and Nicaragua have the most expensive prices, while Guatemala has the most advantageous prices. The average nodal price in the Central American region is 113.78 USD/MW. Nodal prices are generally higher during hours of high demand compared with periods of minimal demand. Honduras and Nicaragua have significant power system losses, reaching up to 17.5% at the regional level.

The process of both charging and discharging electrochemical storage must be executed during periods when there is minimal demand and relatively inexpensive electricity prices. Although Panama is not an optimal choice for storage due to the distance, it could potentially be utilized for delivering energy to Nicaragua. However, there are technical restrictions on transferring electricity between these two countries due to limitations in the regional transmission line attributed to operational security issues.

The regional transmission line has a design capacity of 300

M

V

A

, but technical issues restrict the amount of power that can be transmitted throughout the region, as shown in Table 1. This restricts the flow of power between the northern block (which consists of Guatemala, El Salvador, and Honduras) and the southern block (which includes Nicaragua, Costa Rica, and Panama). Costa Rica, because of its close vicinity, has an opportunity to contribute.

Nonetheless, if there is a desire to balance the energy toward the northern block, the energy must pass via a system that has major losses, such as Nicaragua and Honduras. Guatemala and El Salvador are favorable alternatives for Honduras due to their ability to transfer power and proximity to one another. It is crucial to note that the initial evaluation will focus on the first 15 possible places for an EESS, as depicted in Figure 6.

Table 1 shows the first 15 options, indicating the necessity for a facility with a minimum capacity of 200

M

W

to manage any unforeseen power outage or contingency. In this instance, the selected capacity seeks to mitigate the effects of the power loss due to a failure (a loss of 169

M

W

) while also providing supplementary backup capacity for maintenance activities or unexpected additional demand. The buses carry the prefixes of their respective countries: Guatemala (GUA), Honduras (Hon), El Salvador (SAL), Nicaragua (NIC), Costa Rica (CRC), and Panama (PAN).

However, the actual number of storage locations can differ depending on additional factors, particularly costs to be considered. The sizing analysis takes into account the significance for avoiding a substantial increase in short-circuit current levels at a particular place within the system. The 30

M

W

storage modules, which represent the suggested maximum value, can be effortlessly relocated to various geographical areas within the system without incurring any additional costs. Substations are capable of effectively handling this electrical power as they have been built for these units.

The results suggest that, in terms of storage capacity, the systems of Nicaragua and Panama rank ahead of the others after the first 15 positions. However, the power system of Nicaragua has an inherent limitation: significant losses take place when transmitting power to the north, specifically in Honduras. Similarly, Panama confronts the same condition when it comes to power transmission.

This investigation was designed to show that EESSs may be applied at locations where distances are short, as losses are an important consideration to take into account. In our analysis, we focused on the power plants with the greatest capacity in each system. El Salvador had the lowest capacity at 180

M

W

, while Costa Rica had the highest generation capacity at

306.86

M

W

. Concerning the load, an analogous evaluation took place, and it is shown that Nicaragua has the lowest capacity of

26.40

M

W

, while Panama has the maximum load capacity installed of 239

M

W

Table 1 displays the siting (

S_{h o} (n)

buses) and sizing

P_{n} (n)

of EESSs for each country in the region. It is important to highlight that it is impractical for Nicaragua to incorporate storage in this scenario because of the significant losses and restrictions on cross-border transfers, which may result in voltage droops and associated repercussions. The methodology focuses mainly on the consideration of generator failure or substantial load shedding. The importance of this aspect is quite readily apparent in the estimation of the sizing.

4.2. Optimal Dispatch and Cost

The EESS in consideration has an average daily utilization of 6 h. The regional EESS’s capacity will be 1200

M

W

h

/200

M

W

, and the total cost of investment is estimated to be USD 403.2 million [16]. In order to install this capability, a price tag of USD 140.91 per

M

W

was forecasted. Table 1 displays the estimated amounts for the Cost of Unit Installation, Power Transfer Unit Cost, and Cost Per Unit of Losses in the set proposal. This study highlights the potential benefits of incorporating EESSs into Central American power grids, which have historically experienced issues, particularly as interconnections with Mexico have increased in regularly. Depending on the incident and its magnitude, these situations have occasionally produced blackouts, such as the one studied and others that have happened previously. Infrastructure investment progress has been sluggish, in contrast to the investments in electricity generation. Consequently, EESSs emerge as a viable option to minimize investment while supporting the integration of increased intermittent generation and guaranteeing stable system operating conditions.

4.3. Status Evaluation

As shown in Figure 6, the values obtained from Table 1, containing the results of the optimal dispatch (

P_{d i s p} (n)

), are inputted into the respective nodes recommended for each area or country. The event was simulated, but the storage was set to release power when the breakdown happened. A detector (control system power flow) was used to monitor the imbalances between power generation and demand.

This detector also monitors the interconnection between Mexico and the region, allowing for the monitoring of power and voltage. Similarly, the power interactions among the areas was closely monitored to ensure that the stated restrictions in the ties were not exceeded. After the EESS was developed and simulated, it was implemented in the power system modeled.

Figure 7 depicts the frequency’s behavior with the proposed solution, revealing a more stable pattern at the regional level. This minimizes the possibility of activating low-frequency protection, which can result in the disconnecting of loads. Regarding power, Figure 8 illustrates a consistent pattern. The connection with Mexico is of the utmost importance, as it serves as an essential aspect of the regional system due to its considerable inertia. Therefore, in the event of any alteration in power, it possesses the ability to mitigate any fluctuations that occur. However, it is considered a violation to have a voltage below 0.97 per unit (pu) and a power over 300

M

W

in the interconnection at the same time, as mentioned earlier. The proposed strategy seeks to maintain the power balance below a specific threshold in order to prevent any disturbance in the operational circumstances of the regional system.

The economic dispatch demonstrates that, at the time of failure, 166.97 MW had been utilized to mitigate the impacts of the power loss of 169 MW. The magnitude of this power remains acceptable throughout the simulation. EESS integration was set to activate 120 ms after the event, taking into account the activation times of the protections and the power breaker interconnections. Thus, it is evident that the power in the interconnection was constantly maintained below 300 MW during the simulation. Despite the behavior that was observed, it is expected that the interconnection with Mexico will help to compensate for these power variations, as it performs like the regulating component for the regional system. It is especially important to take into account that the frequency does not drop to possibly hazardous levels, and the EESS helps avoid load disconnections to maintain the energy balance of the system.

Figure 9 demonstrates that the power system continued in a normal state condition during the simulation of the solution proposed in this paper, as indicated by the green curve. The proposed method safeguards the system’s operational security while increasing its flexibility in transfer capabilities. Consequently, the system can not only regulate and control the power and frequency successfully but also maintain continuity.

5. Conclusions

The proposed methodology for developing and managing EESSs combines optimization techniques with dynamic simulations, responding to both planning and operational requirements. Implementation requires essential data, including demand and generation profiles, the characteristics of generators and transmission lines, and nodal prices. A composite index that evaluates various elements, such as costs and distances, is used to choose the candidate buses, making it easier to decide which nodes are the most suitable for installation. The size of the EESS takes into account the generation and load capacity, along with risks associated with contingencies, aiming for successful stability. A cost analysis was performed to reduce operating expenses through efficient power dispatch. The results permit the optimization of EESS utilization, verifying that power flows stay within designated limits, while enhancing system flexibility to aid management during contingency scenarios. Nonetheless, the methodology may be constrained by insufficient precise information and economic information, highlighting the necessity of a systematic approach centered around data and thorough economic evaluation. The advantages and challenges of implementing EESSs in unstable power systems are recognized, especially for the enhancement of system capacity flexibility.

This solution is appropriate for the Central American Power System and can additionally be adapted to take into account multiple grid circumstances. In comparison to EESSs, which are projected to incur a dispatch cost of 140.91 USD/MWh, other generation technologies in Central America may have variable costs that are significantly higher, particularly in the case of thermal plants. Traditional thermal plants sometimes incur expenses above 150 USD/MWh, attributable to elevated fuel and maintenance costs. Hydroelectric facilities, often more economical with prices ranging from 50–80 USD/MWh, are contingent upon water conditions that may restrict their capability during dry periods. On the other hand, solar and wind facilities provide reduced costs, with prices potentially falling below 100 USD/MWh; however, their intermittency presents supplementary problems to system stability. With an initial investment of USD 403.2 million, EESSs are seen as a good way to store energy during times of low demand and then send it out during times of high prices. This would help reduce the problems and costs that come with other technologies, especially in places like Nicaragua and Costa Rica where nodal prices are high.

The proposed approach aims to enhance the operational flexibility of power grids through the utilization of EESSs. It necessitates evaluating stability and operational requirements to choose the most advantageous sites for their implementation. Simulations conducted on transmission networks demonstrate that this integration may improve system stability during conditions of instability, hence addressing the research inquiries. The primary aim of the methodology is to enhance power system resilience, particularly during unforeseen and unanticipated hazardous events, by optimizing the allocation, sizing, and management of electrochemical storage.

The proposed method is a systematic approach to grid planning that entails evaluating possible hazards to the system and making economically appropriate decisions for the strategic placement of storage within the grid. EESSs offer multiple benefits, particularly with the rising need for renewable energy and sustainability. These systems provide improved control of supply and demand, providing operational flexibility that is essential during emergencies and in situations of intermittent generation, such as solar and wind energy. Furthermore, EESSs enhance the stability of the electrical system by regulating energy flows, decreasing operational expenses, and reducing losses. The future will see an intensified adoption and optimization of EESSs, propelled by technological advancements and an increasing demand for decarbonization in the energy sectors. This will enhance the integration of renewable sources and enable the creation of more resilient and efficient electrical networks in accordance with global sustainability objectives and the energy transition.

Author Contributions

G.A.G.-R.: conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Wrote the paper. L.G.-S.: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data. J.R.R.-M.: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data. M.Z.L.: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data. C.M.: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data. All authors have read and agreed to the published version of the manuscript.

Funding

This paper was financed by project VIE 5402-1341-1901 of the “Instituto Tecnológico de Costa Rica”.

Data Availability Statement

All data will be provided upon request.

Acknowledgments

The authors would like to thank the Vice-Rectory for Research and Extension, Postgraduate Studies Office and Scholarship Office of the “Instituto Tecnológico de Costa Rica” for funding this research, CENCE-ICE and INGETEAM for supporting this research, and CFS SISTEMAS S.A. (exclusive representative of ETAP for Costa Rica) and ETAP for providing the academic software license used in the simulations presented in this document.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

The following abbreviations and Spanish acronyms in this manuscript are used:

BESS	Battery Electrochemical Storage System;
EESS	Strategy for an Electrochemical Energy Storage System;
ETAP	Electrical Transient and Analysis Program;
MER	Regional Electricity Market;
SIEPAC	Electrical Interconnection System for the Central American Countries.

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Figure 1. Methodological framework for improving the flexibility of transfer capabilities among various areas.

Figure 2. Interconnection voltage behaviour without electrochemical storage.

Figure 3. Interconnection power behaviour without electrochemical storage.

Figure 4. Seven states sequence of the collapse explained in case study.

Figure 5. Interconnection frequency behaviour without electrochemical storage.

Figure 6. Siting and sizing for electrochemical storage in Central American power system according to Table 1.

Figure 7. Interconnection frequency behaviour with electrochemical storage.

Figure 8. Interconnection power behaviour with electrochemical storage.

Figure 9. Sequence of power system states shown in case study and proposed solution.

Table 1. Methodology results for Central American power system.

n	Bus	$C_{inst}$ $\frac{USD}{MWh}$	$C_{ptc}$ $\frac{USD}{MWh}$	$C_{loss}$ $\frac{USD}{MWh}$	$P_{n} (n)$ MW	$P_{disp} (n)$ MW	$C_{inst}$ $\frac{USD}{h}$	$C_{ptc}$ $\frac{USD}{h}$	$C_{loss}$ $\frac{USD}{h}$	$C_{tot}$ $\frac{USD}{h}$	UGC $\frac{USD}{MWh}$
1	CRC-3	168.14	2.28	0.25	13.52	13.52	2273.80	30.86	3.36	2308.02	170.67
2	CRC-4	168.14	2.28	0.25	13.52	13.52	273.80	30.86	3.36	2308.02	170.67
3	HON-3	150.84	2.28	0.74	12.13	12.13	1830.03	27.68	9.00	1866.70	153.86
4	HON-4	150.84	2.28	0.74	12.13	12.13	1830.03	27.68	9.00	1866.70	153.86
5	HON-1	150.84	2.28	0.74	12.13	12.13	1830.03	27.68	9.00	1866.70	153.86
6	HON-2	150.84	2.28	0.74	12.13	12.13	1830.03	27.68	9.00	1866.70	153.86
7	PAN-2	174.06	2.28	0.30	14.00	14.00	2436.82	31.94	4.25	2473.02	176.64
8	GUA-2	367.60	2.28	0.31	29.57	12.73	4681.06	29.05	3.92	4714.04	159.43
9	SAL-1	75.35	2.28	0.31	6.06	6.06	456.64	13.83	1.87	472.33	77.94
10	SAL-2	75.35	2.28	0.31	6.06	6.06	456.64	13.83	1.87	472.33	77.94
11	GUA-1	367.60	2.28	0.31	29.57	13.40	4926.13	30.58	4.13	4960.83	167.78
12	SAL-3	75.35	2.28	0.31	6.06	6.06	456.64	13.83	1.87	472.33	77.94
13	SAL-4	75.35	2.28	0.31	6.06	6.06	456.64	13.83	1.87	472.33	77.94
14	CRC-1	168.14	2.28	0.25	13.52	13.52	2273.80	30.86	3.36	2308.02	170.67
15	CRC-2	168.14	2.28	0.25	13.52	13.52	2273.80	30.86	3.36	2308.02	170.67
	Regional	165.77	2.28	0.41	200.00	166.97	30,285.50	381.04	69.21	30,735.74	140.91

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MDPI and ACS Style

Gómez-Ramírez, G.A.; García-Santander, L.; Rojas-Morales, J.R.; Lazkano-Zubiaga, M.; Meza, C. Electrochemical Storage and Flexibility in Transfer Capacities: Strategies and Uses for Vulnerable Power Grids. Energies 2024, 17, 5878. https://doi.org/10.3390/en17235878

AMA Style

Gómez-Ramírez GA, García-Santander L, Rojas-Morales JR, Lazkano-Zubiaga M, Meza C. Electrochemical Storage and Flexibility in Transfer Capacities: Strategies and Uses for Vulnerable Power Grids. Energies. 2024; 17(23):5878. https://doi.org/10.3390/en17235878

Chicago/Turabian Style

Gómez-Ramírez, Gustavo Adolfo, Luis García-Santander, José Rodrigo Rojas-Morales, Markel Lazkano-Zubiaga, and Carlos Meza. 2024. "Electrochemical Storage and Flexibility in Transfer Capacities: Strategies and Uses for Vulnerable Power Grids" Energies 17, no. 23: 5878. https://doi.org/10.3390/en17235878

APA Style

Gómez-Ramírez, G. A., García-Santander, L., Rojas-Morales, J. R., Lazkano-Zubiaga, M., & Meza, C. (2024). Electrochemical Storage and Flexibility in Transfer Capacities: Strategies and Uses for Vulnerable Power Grids. Energies, 17(23), 5878. https://doi.org/10.3390/en17235878

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