# Integration of Small Hydropower Plants into Microgrids: Overview, Control, and Modeling Challenges

Gerard Beral, Allal Bouzid, Pierre Tsafack, David Tsuanyo, Bruno Estibals, Corinne Alonso

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Gerard Beral, Allal Bouzid, Pierre Tsafack, David Tsuanyo, Bruno Estibals, et al. Integration of Small Hydropower Plants into Microgrids: Overview, Control, and Modeling Challenges. The 26th European Conference on Power Electronics and Applications, GDR SEEDS France & EPE Association, Mar 2025, Paris, France. 10.34746/epe2025-0328. hal-05073804

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# Integration of Small Hydropower Plants into Microgrids: Overview, Control, and Modeling Challenges

Gerard Beral<sup>1,2,4</sup>, Allal Bouzid<sup>1,3</sup>, Pierre Tsafack<sup>4,6</sup>, David Tsuanyo<sup>4,5</sup>, Bruno Estibals<sup>1,2</sup>, and Corinne Alonso<sup>1,2</sup>

<sup>1</sup>LAAS-CNRS, Toulouse, France

<sup>2</sup>UPS, Toulouse, France

<sup>3</sup>ICAM Toulouse, France

<sup>4</sup>UCAC-ICAM, Douala, Cameroon

<sup>5</sup>MINRESI-CNDT, Yaoundé, Cameroon

<sup>6</sup>University of Buea, Cameroon

E-Mail: gberal@laas.fr, allal.bouzid@laas.fr, tsafack.pierre@ubuea.cm, david.tsuanyo@gmail.com, bestibal@laas.fr, corinne.alonso@laas.fr

*Index Terms*—«Microgrid», «Hybrid power integration», «Control strategy», «Hardware-In-the-Loop (HIL)», «System modeling».

*Abstract*—Hydroelectric power is a dispatchable energy source and plays a crucial role in maintaining stability in islanded microgrids through grid-forming control. This paper presents a comprehensive overview of the modeling and control strategies for small hydropower plants (SHPs), their integration into microgrids, and their interactions with other distributed energy resources (DERs), such as solar power. The study focuses on examining key challenges related to stability, modeling, and control of SHPs to promote hybrid power integration within a microgrid. To address these challenges, recommendations for improving the performance of microgrid-based small hydropower plants are proposed, including advanced control techniques and the implementation of Hardware-in-the-Loop (HIL) simulations to enhance the accuracy of modeling, testing, and validation.

## I. INTRODUCTION

CO₂ emissions from fossil-based electricity generation contribute to world climate change. The transition to renewable energy is crucial for achieving the United Nations Sustainable Development Goal 7 [1]. Nevertheless, integrating renewable energy into the traditional grid can lead to instability due to their intermittent nature. The most promising solution for mitigating this issue is the microgrid (MG) concept (Figure 1). It can improve globally the reliability, sustainability and efficiency of electrical grid [2]. Microgrids (MG) are

recognized as a practical and sustainable power supply solution for households in both urban and rural areas [3]. Microgrids could significantly increase access to electricity worldwide, particularly in African regions, where the number of people lacking access to electricity (around 600 million) has remained relatively constant since 2010 [4]. According to CIGRE, MG can be defined as sections of electricity distribution systems containing loads and distributed energy resources (such as distributed generators, storage devices, or controllable loads) that can be operated in a controlled, coordinated way, either while connected to the main grid and/or while islanded [5]. Based on the nature of the current and the voltage delivered, microgrids can be categorized into three types: AC MG, DC MG, and hybrid MG. These microgrid architectures can be selected according to the desired level of optimal integration of distributed energy resources such as small hydro, wind, gensets, fuel cells, and bio-sources.

Small Hydropower Plants (SHPs) have emerged as a crucial component for maintaining the stability and reliability of microgrids due to their dispatchable nature and ability to provide grid-forming support in islanded modes [6]. SHPs offer several advantages, including low environmental impact, operational flexibility, and a rapid response to fluctuations in demand or intermittent generation from sources like solar or wind power.

However, the potential for SHPs remains largely unexploited in many regions, as shown in Table I. Integrating SHPs into microgrids also presents challenges in terms of modeling, control, and coordination. Unlike
most distributed generators (DGs) in microgrids, which rely on power electronic converters and therefore exhibit low inertia, SHPs have high inertia. This difference can lead to control challenges when SHPs interact with other DGs. To ensure optimal integration of SHPs into microgrids, effective modeling and control strategies are essential. These strategies must align with international standards and operational requirements, such as IEEE 1547.

This paper presents the integration of SHPs into microgrids, with a specific focus on modeling and control strategies and addresses key challenges such as stability and interactions of SHP with DGs based power converters. Finally, the paper proposes solutions and recommendations such as advanced control and modeling techniques to improve the accuracy of SHP modeling, testing, and validation in microgrid environments.

<table><caption>TABLE I
GLOBAL SHP DEVELOPMENT STATISTICS IN 2021 [7]</caption><thead><tr><th>Region</th><th>Potential (MW)</th><th>Developed (MW)</th><th>Remaining (MW)</th></tr></thead><tbody><tr><td>World</td><td>229142</td><td>78046</td><td>151096</td></tr><tr><td>Eastern Asia</td><td>75437</td><td>45723</td><td>29714</td></tr><tr><td>Central Asia</td><td>34358</td><td>266</td><td>34092</td></tr><tr><td>South America</td><td>28483</td><td>806</td><td>27677</td></tr><tr><td>South East Asia</td><td>16361</td><td>850</td><td>15511</td></tr><tr><td>Southern Europe</td><td>14746</td><td>6881</td><td>7865</td></tr><tr><td>North America</td><td>11879</td><td>4734</td><td>7145</td></tr><tr><td>Northern Europe</td><td>10805</td><td>4401</td><td>6404</td></tr><tr><td>Western Asia</td><td>7916</td><td>3533</td><td>4383</td></tr><tr><td>Western Europe</td><td>7635</td><td>6514</td><td>1121</td></tr><tr><td>Eastern Africa</td><td>6833</td><td>276</td><td>6557</td></tr><tr><td>Eastern Europe</td><td>4370</td><td>1903</td><td>2467</td></tr><tr><td>Southern Asia</td><td>4203</td><td>697</td><td>3506</td></tr><tr><td>Middle Africa</td><td>1856</td><td>114</td><td>1742</td></tr><tr><td>Central Africa</td><td>1202</td><td>524</td><td>678</td></tr><tr><td>Australia &amp; New Zealand</td><td>795</td><td>327</td><td>468</td></tr><tr><td>Western Africa</td><td>610</td><td>44</td><td>566</td></tr><tr><td>Northern Africa</td><td>520</td><td>112</td><td>408</td></tr><tr><td>Southern Africa</td><td>422</td><td>50</td><td>372</td></tr><tr><td>PICT's</td><td>413</td><td>114</td><td>299</td></tr><tr><td>Latin America and Caribbean</td><td>297</td><td>177</td><td>120</td></tr></tbody></table>

## II. SMALL HYDROPOWER PLANTS IN MICROGRIDS

Hydroelectric power generation is one of the oldest and most reliable forms of renewable energy. It harnesses

<figure><img src="image_2.png" /><figcaption>Fig. 1. Microgrid and components.</figcaption></figure>

the energy of moving water to rotate turbines, which in turn drive generators to produce electricity. Hydropower plants can be classified by their facility types: storage, pumped storage, and diversion (or run-of-river). According to the US Department of Energy, most small hydropower plants are run-of-river, with little or no water accumulation [8]. Definitions of small hydropower vary; however, for simplicity, we distinguish only between large and small hydropower. Several nations in sub-Saharan Africa and Europe define small hydro as having an upper limit of 10 MW [9]. SHPs are particularly suitable for decentralized and local power production, making them an ideal fit for microgrids. In the other hand, using the infrastructure of small hydropower (SHP) plant has a low impact on the environment [10]. A well-installed SHP plant can operate for over five decades with low costs of maintenance operations and without the need for part replacement, as its components have a lifespan of approximately three decades, ensuring sustained efficiency [11].

Integrating Small Hydropower Plants (SHPs) into a Microgrid (MG) helps maintain frequency within acceptable operational limits, which is crucial when the MG operates independently or is disconnected from the main grid [12]. The grid-forming capability of SHPs can be significantly enhanced by using small water reservoirs or batteries, which helps mitigate the effects of sudden fluctuations in demand or generation from intermittent sources like solar and wind. Additionally, this setup provides black-start capability.

However, integrating SHPs into microgrids and
their interaction with other distributed generators (DGs) presents control challenges due to differing inertia characteristics. Power electronic converters, commonly used in many DGs, have low inertia, whereas SHPs provide high inertia [13]. In future systems, inertia can be understood as resistance to frequency changes, provided through energy exchange from both synchronously connected machines (synchronous inertia) and converter-based generation (virtual inertia), helping to counteract frequency shifts caused by power imbalances between generation and demand [14]

### III. MODELING AND CONTROL STRATEGIES OF SHP

Small hydropower plants (SHPs) primarily consist of a governor, which functions as the turbine control system, a microturbine, and a generator that converts mechanical energy into electrical energy. Hydropower models are developed for various applications, including planning, control, and computational fluid dynamics studies [15]. Other models are used for the specific calculation of parameters such as the diameter and thickness of components like the penstock [16]. The operational performance of hydro-turbines largely depends on the characteristics of the water supplied to the turbine. According to [17], key parameters impacting the turbine include water inertia, water compressibility, and the elasticity of the penstock's pipe walls. In the early 1990s, the IEEE working group collected and improved prime mover/energy supply models [18], which can be considered a framework for several studies. [19] reviewed and enhanced these models by examining the characteristics of hydraulic turbines and water. The small-signal stability of these models was also studied, resulting in appropriate models suitable for control and dynamic studies. A decade later, in [20], the authors presented several hydropower plant models, as shown in Fig. 2. This illustration provides an overview of the various hydropower plant models used in research. Linear models use equations that are linearized around a specific operating point and are often applied in control system stability and small-signal stability studies. In contrast, nonlinear models include detailed equations representing mechanical power and the relationships between turbine flow, turbine head, and gate opening.

The linear model of an SHP is described by a transfer function [19]. The following assumptions are considered: negligible hydraulic resistance, inelastic penstock, incompressible water, water velocity proportional to gate opening and net head, and turbine output proportional to head and flow.

$$ \frac{\Delta \bar{P}_m(s)}{\Delta \bar{G}(s)} = a_{23} \frac{1 + \left(a_{11} - a_{13}\frac{a_{21}}{a_{23}}\right) T_w s}{1 + a_{11} T_w s} \quad (1) $$

(1) is a linear model of non-ideal turbine, where the overbar “-” denotes per unit value. The coefficients $a_{11}$ and $a_{13}$ are partial derivatives of flow with respect to head and gate opening, and the coefficients $a_{21}$ and $a_{23}$ are partial derivatives of turbine power output with respect to head and gate opening. These a coefficients depend on machine loading and may be evaluated from the turbine characteristics at the operating point. Equation (1) becomes (2) when considering the turbine as ideal:

$$ \frac{\Delta \bar{P}_m(s)}{\Delta \bar{G}(s)} = \frac{1 - T_w s}{1 + 0.5 T_w s} \quad (2) $$

The linear model represents the small-signal performance of the turbine and is useful for control system tuning using linear analysis techniques, such as frequency response and root locus [21]. However, such a model is inadequate for studies involving large variations in power output and frequency.

Compared to the linear model, a nonlinear model is more suitable for large-signal stability studies and allows for the design of controllers that can handle significant variations in parameters such as voltage and frequency. A nonlinear model, assuming an inelastic water column, is considered and described in [22], [23]. To obtain a nonlinear model, the first step is to determine the exploitable power from a waterfall.

$$ P_t = \rho g Q H \quad (3) $$

Considering a simple hydraulic system with a penstock, unrestricted head and tail race, and either a very large or no surge tank, and assuming a rigid conduit and incompressible fluid, the basic hydrodynamic equations are:

$$ U = K_u G \sqrt{H} \quad (4) $$

$$ P = K_p H U \quad (5) $$

$$ \frac{dU}{dt} = -\frac{a_g}{L}(H - H_0) \quad (6) $$

$$ Q = AU \quad (7) $$
<figure><img src="image_3.png" /><figcaption>Fig. 2. Types of hydropower plants models.</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>Fig. 3. Nonlinear model of hydraulic turbine.</figcaption></figure>

* U - Water speed.

* G - Ideal gate opening.

* H - Hydraulic head at gate.

* $H_0$ - Initial steady-state value of H.

* P - Turbine power.

* Q - Water flow rate.

* A - Pipe area.

* L - Length of conduit.

* $a_g$ - Acceleration due to gravity.

* $K_u, K_p$ - Constants of proportionality.

To develop the widely used nonlinear model shown in Fig. 3, the following equations are formulated to fully characterize the water column and turbine:

$$ \overline{H} = \left(\frac{\overline{U}}{\overline{G}}\right)^2 \qquad (8) $$

$$ \frac{\overline{U}}{\overline{H} - \overline{H}_0} = -\frac{1}{T_w s} \qquad (9) $$

$$ T_w = \frac{LU}{a_g H_r} = \frac{LQ}{a_g A H_r} \qquad (10) $$

$$ \overline{T}_m = \left(\frac{\omega_0}{\omega}\right) \overline{P}_m \left(\frac{P_r}{MVA_{base}}\right) = \frac{1}{\overline{\omega}} (\overline{U} - \overline{U}_{NL}) \overline{P}_r \overline{H} \qquad (11) $$

$$ \overline{G} = A_t \overline{g} \qquad (12) $$

$$ A_t = \frac{1}{\overline{g}_{FL} - \overline{g}_{NL}} \qquad (13) $$

where:

* $T_w$ - Water starting time at rated load.

* $\overline{T}_m$ - Per unit turbine mechanical torque.

* $\overline{P}_r$ - Per unit turbine rating = turbine MW rating / MVA$_{base}$.

* $\overline{U}_{NL}$ - Water speed at no load.

* $\overline{\omega}$ - Per unit speed.

* $\overline{g}_{NL}$ - Gate opening at no load.

* $\overline{g}_{FL}$ - Gate opening at rated load.

For details about the model and the equations above, refer to [19]–[23].

The governor is a crucial component in a hydro-electric power plant [24]. The governor control system regulates the speed of the turbine and generator, ensuring stable and constant operation. Modern hydraulic turbines use digital governors and electrohydraulic PIDs [12]. PI (Proportional-Integral) and simple droop controllers are widely used in the governor control loop for both frequency and voltage regulation. While PI controllers are straightforward to design, their effectiveness can be limited under highly dynamic conditions, such as rapid load changes or fluctuations in water flow. Simple, or conventional, droop control can lead to deviations in voltage and frequency.
<table><caption>TABLE II
COMPARISON OF SOME TECHNIQUES USED FOR GOVERNOR CONTROL [24].</caption><thead><tr><td>Governor control Type</td><td>Advantages</td><td>Disadvantages</td></tr></thead><tbody><tr><td>Model Predictive Control (MPC)</td><td>- Predicts future system behavior for optimal control<br/>- Improves system performance and stability</td><td>- Requires complex mathematical models and computational power</td></tr><tr><td>Adaptive Control</td><td>- Adjusts control parameters in real-time for optimal response<br/>- Adapts to changing operating conditions and disturbances</td><td>- Complexity increases with system dynamics and uncertainties</td></tr><tr><td>Fuzzy Logic Control</td><td>- Provides flexible and intuitive control strategies<br/>- Handles imprecise and complex system behavior</td><td>- Requires careful design of linguistic variables and rules</td></tr><tr><td>Artificial Neural Networks (ANN)</td><td>- Learns from historical data and optimizes control strategies<br/>- Captures complex system dynamics</td><td>- Requires sufficient training data and computational resources</td></tr><tr><td>Genetic Algorithms (GA)</td><td>- Searches for optimal control parameter values<br/>- Suitable for complex and non-linear optimization problems</td><td>- Requires a large number of iterations for convergence</td></tr><tr><td>Particle Swarm Optimization (PSO)</td><td>- Efficiently explores control parameter space<br/>- Finds optimal solutions through swarm behavior</td><td>- Convergence highly depends on swarm initialization and parameters</td></tr><tr><td>Optimal Power Flow (OPF)</td><td>- Determines optimal power generation and control settings<br/>- Ensures efficient operation while satisfying operational constraints</td><td>- Requires accurate system modeling and computational resources</td></tr><tr><td>Robust Control</td><td>- Improves system performance in the presence of uncertainties and disturbances<br/>- Ensures stability under varying conditions</td><td>- Complexity increases with the level of uncertainty and disturbances</td></tr><tr><td>Adaptive Neuro-Fuzzy Inference System (ANFIS)</td><td>- Combines fuzzy logic and neural networks for optimized control<br/>- Captures complex system behavior and adapts to changing conditions</td><td>- Requires careful tuning of fuzzy inference rules and network parameters</td></tr><tr><td>Reinforcement Learning</td><td>- Optimizes control actions through learning from system feedback<br/>- Adapts to changing system dynamics and uncertainties</td><td>- Requires substantial training time and careful selection of reward structures</td></tr></tbody></table>

## IV. IMPROVING SHP INTEGRATION IN MICROGRIDS

The development of hydropower models for control and optimization has progressed through distinct stages, evolving from foundational studies to advanced applications.

From 1992 to 2002, the focus was primarily on establishing a basis for hydro-turbine control models in dynamic studies. During this period, dynamic mathematical models were created for transient and control studies, aiming to understand and manage the behavior of hydro-turbines under various conditions. Efforts also concentrated on providing grid support through hydropower and conducting studies in the frequency

domain, primarily employing classical control methods [19], [20].

Between 2002 and 2025, the emergence of renewable energy sources, distributed generation, and microgrids marked a significant advancement in grid solutions. The importance of grid support from hydropower became increasingly recognized, leading to the development of more sophisticated dynamic models for transient and control studies. These models incorporated both frequency and time-domain analyses, enabling the development and application of advanced control techniques, particularly for inverter-based resources (IBRs). The large-scale integration of renewable energy sources
(RES) reduces the inertia of the power system, resulting in a high rate of change of frequency (ROCOF) and increased frequency deviations [25], [26].

<figure><img src="image_5.png" /><figcaption>Fig. 4. Concept of controller hardware in the loop [29].</figcaption></figure>

In terms of modeling, hardware-in-the-loop (HIL) [27]–[29] and real-time simulation techniques offer substantial improvements in testing and validating models with high accuracy in a flexible, secure, and high-fidelity environment compared to physical or fully simulated prototypes. Testing setups that feature a mix of physical systems and virtual models are collectively referred to as hardware-in-the-loop [29]. In the Controller Hardware-in-the-Loop (CHIL) approach, the actual controller connects to a modeled plant in real-time simulations, enabling closed-loop evaluation before a physical prototype exists, as shown in Fig. 4. Using hardware-in-the-loop (HIL) with appropriate benchmark systems, such as the CIGRE European LV distribution network illustrated in Fig. 5, improves the test fidelity and improves the understanding of the challenges associated with the integration of distributed energy resources (DERs) in a microgrid. This benchmark LV network maintains key technical characteristics of an actual utility grid, enabling efficient modeling and simulation of various DER technologies [30]. In [31], a hardware-in-the-loop (HIL) solution was developed to successfully test and verify an industrial microgrid configuration. Similarly, [32] presents simulation results for SHP-based systems validated through a hardware-in-the-loop experimental setup. As a result, HIL modeling can be leveraged to enhance the accuracy of SHP models and other DERs, while also playing a significant role in assessing microgrid control units [33].

In terms of control, advanced techniques [34] for frequency and voltage regulation, energy dispatch, and power sharing in microgrids include methods such as model predictive control, adaptive control, sliding mode control, $H_\infty$ control, disturbance estimation techniques, intelligent control methods, advanced droop control with virtual inertia loops, and virtual synchronous generator (VSG) control for primary control, as well as secondary control. For example, as shown in Table II, alternative advanced control methods are presented for governor

<figure><img src="image_6.png" /><figcaption>Fig. 5. Benchmark LV network with DER based on the CIGRE network [30].</figcaption></figure>

control applications. These methods can significantly help address inertia challenges in microgrids that integrate micro-turbines and power converters. Additionally, a hierarchical secondary voltage control approach using communication networks is proposed in [35]. This method accounts for maximum delay bounds and packet losses, which can disrupt real-time system stability. In [36], a hierarchical control strategy is introduced for a hybrid PV-battery-hydropower microgrid, enabling parallel operation of the hydropower and PV-battery systems at different rates while ensuring effective power-sharing among PV voltage-controlled inverters. Developing an SHP model for use in hierarchical control could facilitate the integration of SHPs into microgrids.

Looking towards the future, hydropower models are expected to incorporate even more sophisticated features. Future models will likely include dynamic representations that account for uncertainties, allowing for a more effective response to unpredictable changes in demand or generation. Moreover, adapting advanced control and modeling techniques developed for IBRs to SHPs can facilitate their integration into microgrids.
V. CONCLUSION

Hydroelectric power generation, particularly small hydropower (SHP) systems, is essential to stabilize islanded microgrids through grid-forming control capabilities. This study highlights the evolution of SHP control models, tracing advancements from foundational dynamic studies between 1992 and 2002 to sophisticated modeling for renewable energy integration up to 2024. The increasing role of hydropower in grid support has prompted the development of both frequency and time-domain models, enabling the application of advanced control techniques crucial for stable microgrid operation. Advanced strategies, such as model predictive control, adaptive control, and virtual inertia control, show promise in addressing challenges posed by variable demand, fluctuating water flow, and the integration of other Distributed Energy Resources (DERs) like solar power. To ensure optimal SHP performance within microgrids, several recommendations emerge from this study:

* Adoption of IBRs advanced control techniques and generalized it to microturbines-Generators system, including adaptive droop control and virtual inertia controllers, to enhance SHP resilience against demand and frequency fluctuations.

* Utilization of hardware-in-the-loop (HIL) simulations and real-time models for accurate testing and validation, thereby improving the system's adaptability to real-world microgrid conditions.

* Utilization of CIGRE low voltage network model to test and validate MG advanced controllers.

* Integration of hierarchical with a secondary voltage controls using communication networks, to mitigate issues like time delays and packet loss, which can disrupt system stability.

* Application of data analytics and predictive tools to model SHP behavior under variable conditions, optimizing control strategies and enhancing response time.

By implementing these recommendations, SHP systems can operate as robust, grid-forming resources in microgrids, improving stability, resilience, and efficiency. These advancements ultimately contribute to the development of sustainable and reliable decentralized energy systems.

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