Artículo Académico / Academic Paper

Recibido: 29-04-2026 Aprobado tras revisión: 16-07-2026

Forma sugerida de citación: Sarmiento-Vintimilla, J.; Goercke-Abad, W.; Romero, R. (2026). “Technical Integration Strategies for

Distributed Energy Resources through Virtual Power Plants and Cooperative Microgrids”. Revista Técnica “energía”. No. 23,

Issue I . Pp. 79-90

ISSN On-line: 2602-8492 - ISSN Impreso: 1390-5074

Doi: https://doi.org/10.37116/revistaenergia.v23.n1.2026.761

No Comercial 4.0

Technical Integration Strategies for Distributed Energy Resources through

Virtual Power Plants and Cooperative Microgrids

Estrategias de Integración Técnica de Recursos Energéticos Distribuidos

mediante Centrales Eléctricas Virtuales y Microrredes Cooperativas

J.C. Sarmiento-Vintimilla1, 2

0000-0002-8748-759X

W.A. Goercke-Abad1

0009-0006-5254-532X

R. A. Romero1 0009-0006-9548-4838

1Department of Electronics Engineering, University of Azuay, Cuenca, Ecuador

E-mail: jcsarmiento@uazuay.edu.ec; william.goercke@es.uazuay.edu.ec; andres4482@es.uazuay.edu.ec

2Department of Electrical Engineering, University of the Basque Country UPV/EHU, Bilbao, España

E-mail: jsarmiento002@ikasle.ehu.eus

Abstract

This research proposes a methodology to facilitate the

technical integration of Distributed Energy Resources

through cooperative microgrids and the Virtual Power

Plants concept, promoting the transition from

centralized (Top-Down) models to decentralized

(Bottom-Up) architectures. This approach addresses the

challenges faced by conventional power systems

experiencing an increasing penetration of distributed

generation. To this end, a mathematical optimization

model was developed to coordinate two microgrids

based on the IEEE 9-bus test system. The proposed

methodology was validated using CPLEX optimization

algorithms implemented in GAMS and technical

simulations conducted in DIgSILENT PowerFactory.

The results demonstrate that coordinated operation

among distributed energy units contributes to the

overall energy balance and reduces dependence on

backup thermoelectric generation. Furthermore, the

findings confirm that the strategic integration of

distributed generation enhances the reliability and

resilience of the power system under realistic demand

conditions and the stochastic nature of renewable

generation.

Resumen

Esta investigación propone una metodología para

facilitar la integración técnica de los Recursos

Energéticos Distribuidos, a través de microrredes

cooperativas y el concepto de una Central Eléctrica

Virtual, promoviendo la transición desde modelos

centralizados (Top-Down) hacia arquitecturas

descentralizadas (Bottom-Up). Este enfoque aborda los

desafíos que enfrentan los sistemas eléctricos

convencionales ante una creciente penetración de la

generación distribuida. Con este propósito, se desarrolló

un modelo matemático de optimización para coordinar

dos microrredes basadas en el sistema de prueba IEEE

de 9 barras. La metodología propuesta fue validada

mediante algoritmos de optimización CPLEX

implementados en GAMS y simulaciones técnicas

realizadas en DIgSILENT PowerFactory.

Los resultados demuestran que la operación coordinada

entre unidades energéticas distribuidas contribuye al

balance energético global y reduce la dependencia de la

generación termoeléctrica de respaldo. Asimismo, los

hallazgos confirman que la integración estratégica de la

generación distribuida fortalece la confiabilidad y la

resiliencia del sistema eléctrico frente a patrones reales

de demanda y a la naturaleza estocástica de la

generación a partir de fuentes de energía renovable.

Index terms— Distributed Energy Resources,

Distributed Generation, Microgrids, Optimization,

Virtual Power Plant.

Palabras clave— Centrales Eléctricas Virtuales,

Generación Distribuida, Microrredes, Optimización

Energética, Recursos Energéticos Distribuidos.

Edición No. 23, Issue I, Julio 2026

1. INTRODUCTION

1.1. Problem Identification

In recent decades, power systems have undergone a

significant transformation driven by the increasing

penetration of Distributed Energy Resources (DERs) and

stochastic renewable energy technologies. This

transformation is motivated by both the need for

decarbonization and the pursuit of greater resilience and

operational efficiency. However, the transition from

centralized schemes to decentralized architectures

introduces new challenges related to the operation,

control, and operational planning of power systems.

Traditionally, power system operation has relied on

hierarchical Top-Down centralized models, in which

large-scale power plants supply demand through

unidirectional power flows. In contrast, the widespread

integration of DERs, including photovoltaic systems,

wind generation, small hydropower plants, and Battery

Energy Storage Systems (BESS), introduces

bidirectional power flows and significantly increases the

complexity of operational coordination. This challenge is

further intensified by the intermittent and stochastic

nature of renewable energy sources, making it more

difficult to achieve an efficient dispatch while

maintaining system security.

In general, power systems worldwide have

demonstrated the need to enhance their flexibility and

adaptive capability to cope with scenarios characterized

by high variability in renewable generation. In countries

such as Brazil, Colombia, and Ecuador, drought events

associated with climate change further compromise

system security, particularly due to the high dependence

on hydropower generation. This situation highlights the

need to develop complementary energy alternatives that

are properly integrated into power system planning and

operational requirements.

In this context, DERs have emerged as a strategic

solution to address these challenges. In the Ecuadorian

power system, the contribution of distributed generation

has historically been limited. However, the recent energy

crisis, driven by the country's strong reliance on

hydropower and the severe droughts experienced in

recent years, has accelerated the deployment of DERs

across the residential and industrial sectors, as illustrated

in Fig. 1 [1].

Despite this rapid growth, DERs are generally not

directly managed by the system operator. Their

contribution is typically restricted to supplying energy

whenever the primary energy resource is available, rather

than responding to the operational requirements of the

power system. In the literature, this limitation is

commonly referred to as a lack of technical visibility,

which constitutes a fundamental prerequisite for the

effective technical integration of DERs into modern

power systems.

Figure 1: Evolution of Distributed Generation in Ecuador

Despite this rapid growth, DERs are generally not

directly managed by the system operator. Their

contribution is typically restricted to supplying energy

whenever the primary energy resource is available, rather

than responding to the operational requirements of the

power system. In the literature, this limitation is

commonly referred to as a lack of technical visibility,

which constitutes a fundamental prerequisite for the

effective technical integration of DERs into modern

power systems.

Considering the high vulnerability of the power

system arising from the aforementioned factors, this

paper proposes a mathematical optimization model that

provides both technical visibility and controllability of

DERs through the Virtual Power Plant (VPP) concept.

The proposed approach enables the transition from a

conventional Top-Down power system, where

operational decisions are made in a centralized and

unidirectional manner, to a Bottom-Up architecture, as

illustrated in Fig. 2.

Figure 2: Top-Down vs Bottom-Up architecture

A VPP is an aggregation platform that coordinates the

operation of multiple DERs to optimize their collective

performance and provide grid services as if they

constituted a single generating unit. The individual

resources are interconnected through communication and

control infrastructures, enabling coordinated operation

under a common supervisory framework. In the proposed

methodology, an aggregation agent is responsible for

ensuring the controllability and coordinated dispatch of

DERs while providing the technical visibility required by

the system operator, as shown in Fig. 3. Furthermore,

because a VPP is not constrained by the geographical

Sarmiento et.al / Technical Integration Strategies of Distributed Energy Resources through Virtual Power Plants

location of its constituent resources, it represents a

promising solution for achieving efficient coordination

among cooperative microgrids.

Figure 3: Virtual Power Plant Concept

1.2. Literature Review

The increase in the penetration of renewable energy

sources in modern electrical systems has not only opened

new opportunities but also significant challenges for the

operation, planning, and sustainability of traditional

electrical grids. As decentralized generation paves the

way for models based on DERs, the necessity arises to

implement innovative power control systems that ensure

efficient and resilient management. Many studies have

analyzed the progress in technologies such as VPPs,

microgrids, and energy communities, proposing new

optimization models, architectures, and strategies [2].

In the current energy transition scenario, VPPs have

emerged as a technical solution designed to manage

complex and ever-growing modern electrical systems.

By operating as an aggregation agent, VPPs coordinate

different DERs under a single administrative profile. This

not only allows low-power distributed resources which

are often geographically dispersed to gain visibility for

the system operator, but also opens up new opportunities

for commercialization in wholesale energy markets. The

added value of VPPs lies in their architecture, which

contrasts with traditional systems and, by doing so,

allows for resource coordination from the consumer level

and supports microgeneration sites. It is also noted that a

VPP provides greater flexibility to the system through

control and prediction algorithms that work as a

counterweight to the unpredictability of stochastic

renewable non-conventional resources, while offering

ancillary services such as frequency regulation, voltage

support, and power reserves. [3].

In this context, fractal and holonic architectures stand

out for their capacity to improve the operational

flexibility and scalability of the grid, while allowing

advanced control systems to manage bidirectional power

flow [4]. Along these lines, fractal architectures have

been formalized using the FOCUS system, with the goal

of improving scalability and flexibility in both

microgrids and VPPs [5]. In parallel, the term "holonic"

distinguishes itself by referencing the structural, static

organization of a management system, while the term

"fractal" relates to its dynamic behavior [6].

The fractal principle, although relatively new, has

allowed for significant advancements in distributed

generation; it enables electrical generation near consumer

areas using renewable resources, such as solar energy.

This scheme not only contributes to reducing power

losses but also improves the resilience and reliability of

the grid. Nevertheless, the transition toward this model

comes with its own challenges: infrastructure

traditionally designed for centralized production must be

adapted to numerous small-scale generation units,

bidirectional power flow management, maintaining the

balance between generation and demand, and ensuring a

fail-safe grid. Fractal modeling, based on self-similarity,

offers advantages such as scalability, energy efficiency,

and predictable behavior [7] .

Various authors highlight difficulties in the

integration and optimization of renewable energy

sources. Control systems address issues related to

intermittency and volatility derived from these resources,

whose stochastic nature could lead to forecasting errors,

suboptimal operation, and resource waste [8]. For the

optimal planning of these resources, mathematical

techniques are employed, such as Linear Programming

(LP) and Mixed-Integer Nonlinear Programming

(MINLP), as well as different stochastic programming

models that support VPPs in power dispatch and market

participation under uncertainty [9].

Within this operating scheme, for alternative energy

integration models to be viable, it is mandatory to analyze

the technical response capacity of the virtual power plant.

Sarmiento et al. introduces an evaluation framework

based on several operational flexibility metrics such as

capacity range, ramp rates, and power reserves applied to

both active and reactive power [10]. Complementarily,

Ulbig defines mathematical metrics to evaluate

operational flexibility within the electrical system [11].

The management of said flexibility is not only

fundamental to the internal optimization of the VPP but

also lays the foundation for multisystem cooperation [12]

[13].

Table 1 presents a comparison of the main works

related to the integration of distributed energy resources

through VPPs, cooperative microgrids and fractal

architectures.

Edición No. 23, Issue I, Julio 2026

Table 1: Comparison of related works on the integration of DERs using VPPs

Author/Year

Main focus

Main contribution

Limitations

Vasquez et al.

(2010)

[2]

Hierarchical control of

microgrids

Proposes control strategies for

distributed energy management

in microgrids.

Limited focus on multi-microgrid

coordination.

Bytschkow

(2015)

[5]

Fractal architectures for

Smart Grids.

Develops principles of

composition and scalability for

intelligent grids with a fractal

approach.

It does not include explicit energy

optimization.

Ulbig (2015)

[11]

Operational flexibility of the

electrical system.

Defines mathematical metrics to

evaluate operational flexibility

in electrical systems.

It is not specifically focused on VPPs.

Molotov et al.

(2018) [13]

Microgrid modeling with

renewable energy.

Analyzes operational behavior

of microgrids with renewable

sources.

Limited focus on large-scale

coordination.

Kiriakos and

Zaro (2021)

[12]

Microgrid design for

photovoltaic systems.

It focuses on grid-linked systems

for photovoltaic energy

management.

They lack comprehensive approaches

to load reduction.

Sarmiento et

al. (2022)

[3]

Energy management and

operation of VPP.

It defines VPPs as DER

aggregators to coordinate

resources and participate in

markets.

It defines the coordination strategies for

virtual power plants and addresses the

bottom-up concept. However, it does

not analyze optimization models for the

dispatch of DERs.

Sarmiento et

al. (2024)

[10]

Operational flexibility in

VPP.

Quantify the energy flexibility of

DERs using metrics under

uncertainty.

It focuses on flexibility metrics but

does not develop a coordination

architecture for DER integration.

Al Issa et al.

(2025)

[7]

Grid model based on fractal-

cluster principle.

A fractal architecture can

strengthen the capacity to adapt

to critical conditions.

Centralized infrastructures must adapt

to bidirectional flows.

1.3. Research Contributions

The present work contributes to the advancement and

development of strategies to facilitate the technical

integration of distributed energy resources through the

development of an innovative framework that combines

the concepts of cooperative microgrids and VPPs. Unlike

traditional frameworks, which present limitations in

scalability, multisystem coordination, and operational

efficiency, this research simultaneously addresses these

challenges from a systemic and adaptable perspective:

● An energy management architecture based on

the cooperative microgrids is proposed, which

allows for the structuring of decentralized

electrical systems under hierarchical schemes.

This approach facilitates scalability and

interoperability between multiple microgrids,

promoting an effective transition from

centralized models to bottom-up configurations,

which are more in line with modern electrical

systems.

● A mathematical optimization model is

developed that manages cooperative microgrid

operation through a VPP acting as an

aggregation agent. This model simultaneously

integrates diverse generation sources both

renewable and conventional with BESS,

allowing for efficient power dispatch that

maximizes the use of renewable resources while

minimizing dependency on backup generation.

● A virtual coupling scheme enables energy

cooperation between microgrids without the

need for physical connection between them.

This represents a relevant contribution in terms

of operational flexibility and cost-effective

applicability in real-world systems.

● Finally, the model's validation in an advanced

simulation environment using the IEEE 9-bus

standard system and considering different

operational scenarios demonstrates its technical

feasibility and its capacity to contribute to

operational stability.

These contributions position the proposed approach

as a robust, scalable, and applicable alternative for the

efficient integration of DERs in modern electrical

systems. The rest of this article is organized as follows:

Section 2 presents the proposed methodology, which

includes the system architecture and the formulation of

the mathematical model for the VPP. Section 3 analyzes

the results obtained from the operational coordination of

DERs. Lastly, Sections 4 and 5 present the discussion

and conclusions derived from this research.

Sarmiento et.al / Technical Integration Strategies of Distributed Energy Resources through Virtual Power Plants

2. METHODOLOGY

With the goal of verifying the applicability of the

proposed model in electrical system management, a

modular grid topology was configured applying the IEEE

9-bus standard system as a reference for the operational

behavior analysis as shown in Fig. 4. [14].

Figure 4: IEEE 9-bus standard topology

2.1 Case Study Grid Architecture

The studied system was designed comprising different

sections that interact with one another.

● Main Grid: It is represented as an infinite bus that

performs a dual role. From a technical standpoint, it

serves as a reference for both voltage and frequency

to ensure system stability. From a commercial

perspective, on the other hand, it operates as the

exchange node where the VPP delivers its firm,

constant power block, absorbing the continuous

power supply and generating revenue from the

stability services provided by the microgrids.

● Cooperative Microgrids: Two independent

microgrids were developed (MG1 and MG2); both

integrate distributed generation and are connected

to the main grid for power supply.

Fig. 5 shows the general architecture of the proposed

system, composed of two microgrids interconnected by a

slack bus that acts as a reference node for power

exchange with the main grid.

With the ultimate goal of guaranteeing the feasibility

and reliability of the model, real operational records

provided by the company ELECAUSTRO S.A.

(Ecuador’s public electricity generation company), were

used. Specifically, power generation data from the

Huascachaca wind and solar farm were utilized to

develop the statistical profiles for the model. Although

both microgrids share the same general structure, their

distributed energy resources are not identical. Therefore,

it is necessary to clearly define the location and capacity

of the DERs installed in each microgrid.

MG1 contains photovoltaic generation located at

buses b2, and b3 with a capacity factor of 17% and 150

kW. Wind generation is distributed at buses b3, b4, b5,

b6, and b7 with a capacity factor of 28% and 20 MW.

MG1 includes one hydroelectric unit located at bus b8,

with a nominal power of 20 MW and a maximum daily

available energy of 240 MWh. The storage system of

MG1 is composed of two battery energy storage units

located at buses b5 and b6, each one with 10 MWh of

energy capacity and 5 MW of maximum charging and

discharging power. Therefore, MG1 has a total storage

capacity of 20 MWh and a total charge/discharge power

capacity of 10 MW. Finally, MG1 includes two thermal

backup units located at buses b2 and b3, each one with

20 MW of nominal power, giving a total thermal backup

capacity of 40 MW.

MG2 contains photovoltaic generation located at

buses b2, b3, b4, b5, b6, and b7 with a capacity factor of

16,5% and 355 kW, wind generation in MG2 is

distributed across buses b2 to b9 with a capacity factor of

31% and 36 MW. Hydroelectric generation in MG2 is

represented by three hydroelectric units located at buses

b5, b7, and b8. Each unit has a nominal power of 20 MW,

giving a total hydroelectric capacity of 60 MW. The

maximum daily available hydroelectric energy in MG2 is

840 MWh. The battery storage system of MG2 is

composed of two battery units located at buses b5 and b6,

each one with 12 MWh of energy capacity and 5 MW of

maximum charging and discharging power. Therefore,

MG2 has a total storage capacity of 24 MWh and a total

charge/discharge power capacity of 10 MW. Finally,

MG2 includes three thermal backup units located at buses

b2, b3, and b9, each one with 20 MW of nominal power,

resulting in a total thermal backup capacity of 60 MW.

The demand profile is also distributed between both

microgrids. In MG1, the aggregated demand varies

between approximately 8,16 MW and 40,49 MW during

the 24-hour horizon. In MG2, the aggregated demand

varies between approximately 6,62 MW and 33,08 MW.

Therefore, the total VPP demand ranges from

approximately 14,77 MW to 73,57 MW. This demand is

supplied through the coordinated use of renewable

generation, hydroelectric generation, battery storage,

thermal backup units, energy imports from the main grid,

and cooperation between microgrids.

Simulation Scenarios: Two operational modes were

defined to evaluate the impact of the proposed

architecture:

A. Disaggregated Operation: In this mode, microgrids

MG1 and MG2 operate as completely isolated

entities. Each microgrid optimizes its dispatch to

minimize its own operational costs without

allocating the surplus of one grid to cover the deficit

of the other. Any energy imbalance is addressed by

purchasing energy directly from the main grid.

B. Coordinated Operation: By enabling joint

optimization, the model seeks a global system

balance, allowing the energy injection from one

microgrid into the main grid to be recognized as a

supply for the other. This results in a more efficient

system and minimizes net dependency on external

sources.

Edición No. 23, Issue I, Julio 2026

Figure 5: Energy cooperation between microgrids

2.2 Proposed Mathematical Model

The operational coordination of the proposed

resources is formulated as a mathematical optimization

problem aimed at determining the optimal generation

dispatch that satisfies commercial commitments while

minimizing the total operating cost, denoted by . The

objective function comprises the following cost

components: , the cost associated with renewable

energy generation (solar and wind); , the operating

cost of hydropower generation; , the cost associated

with the operation of the BESS, including charging and

discharging processes; , the operating cost of thermal

generating units, including generation and start-up/shut-

down costs; , the net cost of energy exchange with the

main grid; , the cost associated with power exchange

between microgrids; , the overall cost of coordinated

system operation; and, , the costs associated with

operational constraints and model penalty terms.

        

   (1)

2.2.1 Virtual Coupling and Power Delivery

To represent the aggregated operation of the

cooperative microgrids, the net power injected by the

VPP into the main grid is defined. This magnitude is

expressed as the difference between the power exported

and imported by the microgrids during each hourly

interval:

 



  





 (2)

where  represents the net power exchanged between

the VPP and the main grid at hour h and where the

microgrids are represented as   , 



 is the

exported power and 



 is the imported power at a

period h of time.

For this case scenario, a firm, constant power delivery

commitment is imposed on the VPP, such that the net

power exported to the main grid maintains a flat profile

throughout the entire operational horizon:

  

  (3)

where, 

corresponds to the power delivery target

committed to the system operator. In the developed case

study, a fixed delivery of 3 MW is adopted for each hour

of the planning horizon.

2.2.2 Export Breakdown:

With the objective of tracing the energy origin of the

power delivered by the VPP, the total export from each

microgrid is divided into a renewable component and a

thermal component:





  



  



   (4)





 



  



  



  





 (5)

where 



represents the fraction of exports

originating from renewable resources and storage,

composed as follows: 



 represents all photovoltaic

power generation exports, 



 represents all wind

power generation exports, 



 represents all

hydropower generation exports and 



 represents

the batteries’ discharge power exports on a specified bus

b, while 



 represents the fraction associated

with thermal generation exports. This decomposition

allows for verifying, within the results analysis, whether

the delivery committed to the main grid is covered by

renewable generation or if it requires thermal support

during specific hours.

2.2.3 Operational Constraints

Nodal Power Balance





  



  



  

  







  



  



      

  



 





 (6)

This equation represents the systems nodal balance

where 

 is the total power produced by thermal

backup generators, 



 represents all power imported

from the main grid and 



 is the power received

from the other microgrid as cooperation.

Sarmiento et.al / Technical Integration Strategies of Distributed Energy Resources through Virtual Power Plants

  

 stands for the active power

flow through transmission lines (l) for each microgrid in

each bus in a specified period of time.  represents

the load shedding,  represents the internal power

demand, 

 is the power used to charge the batteries,





 is the exported power to the main grid, 



 is

the power sent from one microgrid to the other as

cooperation for each microgrid in each bus at each period

of time.

2.2.4 Generation Constraints

Power Limits:





      



   (7)

Where 



 and 



 are the minimum and

maximum capacities at which the generator operates, 

is a binary variable used to tell if the machine is on (1) or

off (0) and  is the power generation at a period of time

Ramp-Up Rate:

    

    



     (8)

Ramp-Down Rate:

    

    



     (9)



 and 

 they indicate how quickly a generator can

increase or decrease its power output from a    to a 

period of time.

2.2.5 Storage Dynamics

Energy Balance:

     

  (10)

 represents the state of charge at a specific period

of time.  and  are the charge and discharge

efficiencies

Exclusivity Constraint:

    

   (11)

    

     (12)

and  represent power charge and discharge,

while 

 and 

 represent the maximum charge and

discharge levels allowed.  is a binary state variable

to forbid simultaneous charge and discharge.

Capacity and Boundary Limits:

  (13)

Forces the algorithm to charge the battery to at least

its initial levels at the end of the 24-hour operational

horizon.

3. RESULTS ANALYSIS

3.1 Scenario 1: Disaggregated Operation:

Initially, the operational behavior of MG1 and MG2

was evaluated in an isolated manner. Under this

traditional scheme, the optimization algorithm

minimized operational costs independently, forcing each

microgrid to satisfy its own demand through local

resources. If a power deficit occurred, the microgrid was

obligated to buy power from the main grid at market

prices or rely on backup thermal generation. Fig. 6 shows

the combined operation of the two microgrids to meet

demand. In this scenario, there is a need for thermal

generation (red bars) and for purchasing energy from the

grid (orange bars).

Under this operating scenario, renewable energy

sources supply 80.47% of the total demand, with

hydropower representing the largest share at

approximately 55.42%. The remaining 19.53% of the

energy balance is met through backup thermal

generation.

Since this scenario lacks a cooperative design, the

results highlight the typical inefficiency of distributed

generation systems when operating without coordination.

This operational case serves as a baseline to later

compare the benefits of a coordinated architecture. In

qualitative terms, it represents the typical behavior of two

isolated microgrids lacking an integration mechanism,

where system balance is guaranteed only locally rather

than globally.

Figure 6. Operation of the system without internal cooperation

3.2 Scenario 2: Coordinated Operation between

Microgrids

This scenario enables coordinated cooperation

between MG1 and MG2 through the proposed

architecture, with the VPP acting as the aggregating

agent. Under this configuration, the microgrids cease to

operate independently and converge into a single

aggregated entity governed by a joint operational

scheme. This strategy allows surpluses from one

microgrid to cover the deficits of the other, enabling the

two grids to function as interconnected parts of a whole.

Consequently, this integration provides greater flexibility

and maximizes the exploitation of available renewable

resources.

Edición No. 23, Issue I, Julio 2026

Figure 7: Comparison between total demand and total generation of the coordinated system

Figure 8: Hourly generation dispatch in microgrid MG1

Figure 9: Hourly generation dispatch in microgrid MG2

Sarmiento et.al / Technical Integration Strategies of Distributed Energy Resources through Virtual Power Plants

The comparison between the total demand and the

total generation of the system is illustrated in Fig. 7. It

can be observed that both magnitudes are equivalent

throughout the 24-hour operational horizon, with the

exception of the generation peak occurring in t14. These

peaks correspond to the periods in which the Battery

Energy Storage System is being charged, thereby

ensuring an adequate energy balance. Furthermore, it is

noted that the total system demand reaches its maximum

values in t20, defining the critical period of the

operational horizon. This result confirms that the

optimization model effectively coordinates all available

renewable resources and backup plants to satisfy the

aggregated demand, demonstrating that the virtual

coupling imposed by the VPP functions correctly.

The hourly dispatch for MG1 is presented in Fig. 8.

Individually, this microgrid does not have sufficient

renewable generation capacity to satisfy its own demand.

Therefore, under the disaggregated operating scenario, it

must rely on backup thermal generation and energy

imports from the main grid to meet its energy

requirements. In contrast, under the proposed cooperative

operation framework, the dispatch of MG1 is optimized

by exploiting the energy complementarity provided by

MG2, thereby avoiding the use of higher-cost energy

alternatives. It is observed that during morning hours,

there is a significant contribution from wind generation,

complemented to a lesser extent by photovoltaic

generation and, as the day progresses, by hydroelectric

generation. During these periods of high resource

availability, the system utilizes the surplus to recharge

the BESS, which will be deployed in subsequent periods.

On the other hand, Fig. 9 illustrates the generation

dispatch for microgrid MG2. This microgrid has a high

installed capacity of renewable energy resources. Under

the disaggregated operating scenario, surplus energy

must be traded in electricity markets to avoid renewable

energy curtailment. However, this approach entails

economic risks associated with market participation and

depends on effective commercial integration. Since this

research focuses exclusively on the technical integration

of DERs, the operational flexibility of MG2 is exploited

to support the energy deficit of MG1, thereby

maximizing the utilization of the available renewable

resources. In this case, the significant contribution of

hydroelectric generation is notable, acting as a flexible

support source to cover the local demand and contribute

to the energy cooperation scheme. This strategic

management of water resources makes MG2 a key node

within the global coordination; since hydroelectric plants

possess regulation capacity, they act as a compensation

mechanism against the stochastic variability of non-

conventional renewable generation, highlighting energy

complementarity as a cornerstone of the proposed

cooperative model.

Table 2 shows a summary of the most significant

quantitative results of the cooperative scenario.

Table 2: Cooperative Microgrid Scenario Results

Parameter

Value

Description

Total Demand

14.77-73.57

Variation in aggregate

demand over the 24-

hour horizon.

Total

Hydroelectric

Generation

762,12

MWh

Total hydroelectric

generation under the

coordinated operation

scheme.

Renewable

Energy Share

99,94 %

Share of energy supply

from renewable

sources in the

cooperative scenario.

Contribution of

Battery Energy

Storage Systems

(BESS)

0,06 %

Contribution of

batteries to the total

energy supply in

coordinated operation.

3.3 Role of the VPP in the Cooperative Microgrid

Operation

VPPs have emerged as an effective strategy to

facilitate the technical and commercial integration of

DERs into power systems. Their operation relies on an

aggregation agent capable of coordinating multiple

microgrids, optimizing the available resources, and

exploiting the energy complementarity among different

generation technologies. However, this study focuses

exclusively on the technical benefits that VPPs provide

to power system operation rather than on their

participation in electricity markets. Although the

proposed optimization model aims to minimize operating

costs, the primary role of the VPP is to maximize the

utilization of renewable energy resources. This objective

is achieved through the coordinated dispatch of

technologies with near-zero variable costs, such as

photovoltaic and wind generation, very low variable

costs associated with hydropower, and comparatively

high variable costs corresponding to thermal generation

and energy imports from the main grid.

The results demonstrate that coordinated microgrid

operation significantly improves the overall performance

of the system. Although solar and wind generation

remained unchanged in both operating scenarios, their

utilization increased by 22% due to the reduction of

renewable energy curtailment. Furthermore, hydropower

production increased from 566,33 MWh to 762,12 MWh

over the 24-hour operating horizon, completely

eliminating the need for backup thermal generation.

Under the disaggregated operating scenario, 199,56

MWh of backup thermal generation was required to

satisfy system demand. These results demonstrate that

energy exchange between microgrids enables the system

to compensate for energy deficits by utilizing available

renewable resources that would otherwise be curtailed,

thereby eliminating the dependence on backup thermal

generation. Consequently, the share of renewable

Edición No. 23, Issue I, Julio 2026

resources in the energy mix increased from 80,47% in the

baseline scenario to 99,94% under cooperative operation,

while BESS contributed only 0,06% of the total energy

supplied.

Overall, the results demonstrate that coordinated

operation transforms independent microgrids into a more

robust cooperative energy system. Integrating DERs

through a VPP enables more effective utilization of

surplus renewable energy, maintains the overall energy

balance, and supports sustained energy exports to the

main grid while reducing reliance on high-cost backup

generation.

Table 3 shows the most significant benefits of the

cooperative scenario compared to the disaggregated

scenario.

Table 3 : Disaggregated Scenario vs Coordinated Scenario

Performance

variable

Disaggregat

ed Scenario

Cooperativ

e Scenario

Impact

Backup

thermal

generation

199,56

MWh

0 MWh

Complete

elimination

of thermal

backup

dependency

Hydroelectric

Generation

566,33

MWh

762,12

MWh

Increase of

195,79

MWh to

meet the

demand of

both

microgrids.

Share of

Renewable

Resources

80,47 %

99,94 %

19,47%

increase in

the system's

energy mix.

Finally, an additional validation stage was carried out

in DIgSILENT PowerFactory as part of the proposed

methodological framework. The simulation results

confirm that the power flow converges under the

coordinated operating scheme and that voltage profiles

remain within the permissible limits (±5%) at all system

buses. In this regard, the validation is not intended to

provide a detailed numerical assessment but rather to

verify the technical feasibility of the proposed topology

under realistic operating conditions. Such analyses

constitute an essential step that the VPP operator should

perform to ensure the secure and reliable operation of the

power system.

In this context, the VPP plays a key role in

coordinating DERs, including the indirect management

of reactive power. Through the coordinated control of

distributed generation, energy storage, and conventional

generation units, the VPP contributes to maintaining

acceptable voltage levels, improving power flow

distribution, and reducing system losses. This capability

becomes particularly important in power systems with

high penetration of renewable energy resources, where

coordinated operation enhances both operational

efficiency and system resilience.

4. DISCUSSION

DERs have great potential to contribute to the

operation of the electrical system. However, the lack of

technical integration means that these units are

underutilized. The results of this research imply not only

an improvement in operational indicators, but also a

change in the way DERs interact with each other and with

the main grid. Rather than simply improving operational

indicators, coordinated operation fundamentally changes

the interaction between DERs and the main grid. The

difference between the analyzed scenarios is significant;

transitioning from an isolated to a coordinated operation

implies moving from local inefficient decisions toward a

systemic logic where global benefit takes precedence

over the individual. In the disaggregated scenario, the

system's behavior reproduces the typical limitations of

distributed generation when an effective coordination

mechanism is absent.

Each microgrid responds to its own constraints

without considering the state of the system as a whole,

leading to an inefficient use of available resources. This

result is consistent with existing literature, where the lack

of integration among DERs typically results in higher

operational costs and an unnecessary reliance on backup

generation. In contrast, coordinated operation

demonstrates that even without physical modifications to

the infrastructure, the mere introduction of an intelligent

aggregation scheme can substantially change global

performance.

One of the most relevant aspects is the model's ability

to exploit the energy complementarity among sources.

The interaction between wind, solar, and hydroelectric

generation, coupled with energy storage support, not only

allows for efficient demand coverage but also

substantially reduces the use of thermal generation in the

case study. This result should be interpreted with caution;

while it responds to the specific conditions of the

modeled system, it highlights the real potential of

coordinated schemes to displace costlier and more

polluting technologies when flexibility is adequately

managed.

Along these lines, the role of hydroelectric generation

as a regulating resource deserves special attention. Far

from acting solely as a baseload source, its role within the

proposed architecture is closer to that of a dynamic

balancing mechanism, capable of compensating for the

variability of non-conventional sources. This behavior

reinforces the idea that the integration of DERs does not

depend exclusively on adding more installed capacity,

but rather on the intelligent management of existing

resources.

Sarmiento et.al / Technical Integration Strategies of Distributed Energy Resources through Virtual Power Plants

Another outstanding result is the VPP's ability to

sustain a constant power delivery to the main grid,

despite the inherently variable nature of renewable

resources. This finding is particularly significant, as it

addresses one of the most frequent criticisms of

distributed generation: its perceived inability to provide

firm power services. What the model demonstrates is

that, under an adequate aggregation and coordination

scheme, individual variability can be smoothed at the

aggregated level, resulting in more stable and predictable

generation profiles.

From a methodological standpoint, the adoption of a

cooperative principle introduces a differentiating element

compared to conventional approaches. More than a

simple hierarchical structure, the proposed scheme

suggests an organizational logic in which each level of

the system replicates similar operational patterns, thereby

facilitating scalability. This is particularly relevant in

scenarios where the expansion of DERs does not follow

centralized planning but rather responds to local and

heterogeneous dynamics. In this sense, the proposed

architecture not only solves an immediate technical

problem but also provides a conceptual framework for

the orderly growth of increasingly distributed power

systems.

Nevertheless, it is important to recognize certain

limitations of this study. The model focuses on technical

integration and does not explicitly incorporate market

signals, which could influence the economic viability of

operational decisions. Furthermore, while the AC

environment validation confirms the electrical feasibility

of the dispatch, the real-world implementation of

coordination schemes like the one proposed would

require more advanced communication and control

infrastructures, as well as regulatory frameworks that

facilitate the aggregated operation of DERs.

The results suggest that the challenge of integrating

distributed resources lies not only in their quantity or

diversity but in the system's ability to coordinate them

effectively. The evidence presented reinforces the idea

that future solutions will not solely involve expanding

infrastructure, but rather redefining the control and

operational architectures that allow for maximizing the

exploitation of available flexibility.

5. CONCLUSIONS

The development of this work allows for the

extraction of a central idea that permeates the entire

research: the effective integration of DERs depends not

only on their technological incorporation, but also on the

way they are organized and coordinated within the power

system. Under this premise, the proposed architecture

based on the use of a VPP proves to be a coherent

alternative to the current needs of increasingly

decentralized systems.

One of the most consistent findings is the substantial

difference between operating microgrids in isolation

versus under a coordinated scheme. While the former

reproduces known inefficiencies such as greater

dependence on the main grid and unnecessary use of

backup generation, the latter demonstrates that energy

cooperation enables a more intelligent exploitation of

available resources. This improvement is not marginal

but structural, as it transforms the operational logic from

a local approach to a global perspective.

Furthermore, the model confirms that the aggregation

of DERs can overcome one of the primary barriers

attributed to renewable energy: the variability and

stochasticity of the primary resource. Far from

representing an insurmountable limitation, the results

show that, through coordination and storage, it is possible

to construct stable and reliable generation profiles. The

ability to maintain a constant power delivery to the main

grid is, in this sense, a particularly relevant outcome, as

it brings distributed systems closer to the operational

standards traditionally required of conventional

generation.

Another aspect worth highlighting is the role of

energy complementarity. The interaction between

different technologies, especially between variable

renewable sources and flexible hydroelectric generation

allows not only for demand coverage but also for the

optimized use of resources. This behavior suggests that

the value of DERs lies not only in their individual

contribution but in their ability to integrate within

coordinated schemes that enhance their strengths and

mitigate their limitations.

From a methodological point of view, the model's

validation in an AC simulation environment reinforces

the robustness of the proposal. It is not merely a

theoretical result but a scheme that respects the physical

constraints of the system and can be interpreted within a

real operational context. This provides the level of

credibility necessary to consider its application in

broader scenarios.

Nevertheless, as with any research approach, this

work also opens new questions. The incorporation of

economic signals, interaction with electricity markets,

and communication infrastructure requirements

represent natural lines of evolution. In particular, the

practical implementation of such architectures will

require regulatory frameworks that recognize the value of

aggregation and flexibility as essential system services.

In summary, the presented proposal not only validates

the technical feasibility of integrating DERs through

cooperative schemes but also suggests a paradigm shift

in the planning and operation of power systems. Within

the context of the energy transition, where

decentralization is increasingly evident, moving toward

more flexible, scalable, and coordinated organizational

models is no longer an option but a necessity.

Edición No. 23, Issue I, Julio 2026

6. REFERENCES

[1] Agencia de Regulación y Control de Electricidad,

“Atlas del Sector Eléctrico Ecuatoriano 2024”, vol.

1, p. 109, mar. 2025.

[2] J. Vasquez, J. Guerrero, J. Miret, M. Castilla, L.

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[3] J. C. Sarmiento-Vintimilla, E. Torres, D. M.

Larruskain, M. J. Pérez-Molina, “Applications,

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Virtual Power Plants as a Strategy to Facilitate the

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[4] E. Ortjohann et al., “Cluster fractal model — A

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in Proc. 2013 Int. Conf. Clean Electrical Power

(ICCEP), Alghero, 2013, pp. 293–297.

[5] D. Bytschkow, “Towards composition principles and

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[6] G. Florea, O. Chenaru, D. Popescu, R. Dobrescu, “A

Fractal Model for Power Smart Grids”, in Proc. 20th

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[7] H. A. Al_Issa et al., “Model of the electric network

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[8] J. L. Sampietro P. Pico Valencia, “Revisión

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micro-redes de energía”, MASKAY, vol. 8, no. 2, p.

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Hasanzadeh, S. B. Elghali, “Virtual Power Plant

Operational Strategies: Models, Markets,

Optimization, Challenges, and Opportunities”,

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Torres, O. Abarrategi, “Assessment of the

operational flexibility of virtual power plants to

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Systems”, Ph.D. dissertation, Dept. IT & Elect. Eng.,

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Juan C. Sarmiento-Vintimilla.-

He received his degree in Electrical

Engineering from the University of

Cuenca in 2011 and his Master’s

degree in Integration of Renewable

Energy into the Electrical System

from the University of the Basque

Country / Euskal Herriko

Unibertsitatea in 2013. He is currently a Ph.D. candidate

in Electrical Energy Systems at the University of the

Basque Country / Euskal Herriko Unibertsitatea. He

currently works as a Planning Engineer at

ELECAUSTRO S.A. and as a lecturer at Universidad del

Azuay. His research interests focus on the integration of

renewable energy into power systems, virtual power

plants, and operational flexibility.

William A. Goercke-Abad.- Born

in Cuenca, Ecuador in 2002, he is

currently completing his studies in

Electronics Engineering at

Universidad del Azuay. His areas of

academic interest focus on

autonomous renewable energy

systems, microgrid modeling, and

electrical power systems, with a particular emphasis on

the integration of distributed energy resources.

Roberth A. Romero. Born on

January. 1992, he is currently

studying Electronics Engineering at

Universidad del Azuay. His

academic interests focus on

developing intelligent solutions for

energy systems, integrating

optimization tools, computational

analysis, and emerging technologies. During his

academic training, he has worked on the modeling and

optimization of microgrids, exploring strategies for

coordinated operation and efficient resource

management using mathematical and computational

tools.