Report on Adaptive Controller for Power Flow Management in Microgrid Systems with Emphasis on Sustainable Development Goals (SDGs)

Abstract

This report presents the development of an efficient controller for DC Microgrid systems aimed at optimizing power flow management among distributed energy resources (DERs). The study focuses on the operation of a central controller to maximize energy harvesting from solar and wind sources and manage bidirectional power flow with battery storage to maintain a constant DC bus voltage. Initial implementation of a Proportional-Integral (PI) controller using real-time laboratory data revealed error indices around 30%. The introduction of a Model Reference Adaptive Controller (MRAC) significantly reduced these errors, enhancing grid control and meeting customer requirements dynamically. This advancement supports the achievement of multiple Sustainable Development Goals, particularly SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action).

Introduction

Renewable energy sources are projected to dominate electricity production in the coming millennium. The Earth’s surface receives approximately 3.2 EJ/year of energy, a fraction of which can address global energy crises sustainably. Photovoltaic (PV) systems convert solar energy into electrical energy, while wind energy systems harness wind power generated by uneven solar heating. This report analyzes hybrid renewable energy systems integrating solar, wind, and battery storage within a DC microgrid framework, emphasizing efficient power management and sustainability.

Key Components and Concepts

• Photovoltaic arrays composed of series and parallel-connected cells optimize voltage and current output.
• Wind energy conversion systems (WECS) utilize permanent magnet synchronous generators (PMSG) for variable speed operation.
• Bidirectional DC–DC converters manage power flow between sources, storage, and loads.
• Maximum Power Point Tracking (MPPT) controllers maximize energy extraction from renewable sources.
• Battery Energy Storage Systems (BESS) provide reliability and stability, mitigating intermittency.

Relevance to Sustainable Development Goals

• SDG 7: Promotes access to affordable, reliable, sustainable, and modern energy through renewable integration.
• SDG 9: Encourages innovation in infrastructure and industrial technologies via advanced control strategies.
• SDG 13: Supports climate action by reducing reliance on fossil fuels and lowering greenhouse gas emissions.

PV Cell Modeling and Equations

The photovoltaic cell operates as a semiconductor diode exposed to light, generating photocurrent. The model includes equations for photocurrent, diode current, shunt current, and output current, accounting for temperature and irradiance effects. Key parameters such as short-circuit current, temperature coefficients, and diode characteristics are incorporated to accurately simulate PV behavior.

PV Cell Characteristics

• Maximum Power Point (MPP)
• Open Circuit Voltage (Voc)
• Short Circuit Current (Isc)

These characteristics are essential for designing efficient PV modules and are critical for achieving SDG 7 by enhancing renewable energy utilization.

Modeling of the Wind Energy System

Wind energy offers an economical and environmentally friendly power source. The wind turbine converts kinetic wind energy into mechanical power, which is then converted to electrical energy using PMSG. The power coefficient, pitch angle, and tip speed ratio are key factors influencing turbine efficiency.

Wind Energy Conversion System Equation

1. Power output: ( P_w = frac{1}{2} rho S v^3 c_p )
2. Mechanical power and torque calculations based on turbine dynamics.

This system supports SDG 7 and SDG 13 by promoting clean energy generation and reducing environmental impact.

Battery System

Rechargeable batteries store electrical energy chemically, enabling energy supply during low renewable generation periods. Lithium-ion batteries are preferred for their efficiency and longevity. Accurate modeling of battery state-of-charge (SOC), voltage, and current is crucial for system reliability and longevity.

Battery Model Equations

• Open-circuit voltage as a function of SOC.
• Terminal voltage considering internal resistance.
• SOC estimation integrating current over time.

Battery management enhances energy storage efficiency, contributing to SDG 7 and SDG 12 (Responsible Consumption and Production).

System Model

The DC microgrid system integrates PV, wind, and battery storage connected to a 48 V low-voltage DC bus. It can operate in grid-connected or islanded modes, providing reliable and sustainable electricity, especially in rural areas. The system is designed to be robust, adaptable, and cost-effective, aligning with SDG 9 and SDG 11 (Sustainable Cities and Communities).

Power Flow Management Cases

1. Power generation equals demand plus losses.
2. Generation exceeds demand, surplus stored or exported.
3. Generation less than demand, deficit supplied by battery or grid.

Proposed Methodology

Model Reference Adaptive Controller (MRAC)

MRAC dynamically adjusts controller parameters to handle system uncertainties and variations in renewable energy inputs. It uses a reference model to minimize error between desired and actual system outputs, ensuring stability and improved performance over conventional PI controllers.

Adaptive PI Controller

• Control output: ( u(t) = K_p(t) e(t) + K_i(t) int e(t) dt )
• Adaptive gains ( K_p(t) ) and ( K_i(t) ) update based on error minimization laws.
• Ensures system stability and robustness under variable conditions.

Results

Simulations using MATLAB Simulink with real-time solar irradiance and wind speed data demonstrate the effectiveness of the MRAC-based adaptive PI controller in maintaining a stable 48 V DC bus voltage. Key findings include:

• Adaptive PI controller reduces voltage error from approximately 30% (PI controller) to less than 5%.
• Improved state-of-charge management of batteries, enhancing energy storage efficiency.
• Successful power flow management across three cases of generation-demand scenarios.
• Lower Total Harmonic Distortion (THD) in grid voltage with adaptive control (0.26) compared to PI control (0.87), indicating higher power quality.

These results contribute directly to SDG 7 by ensuring reliable and clean energy supply, and to SDG 13 by facilitating the integration of renewable energy sources.

Conclusion

The implementation of a Model Reference Adaptive Controller in a DC microgrid system significantly enhances power flow management, stability, and resilience under variable renewable energy inputs. The adaptive controller outperforms traditional PI controllers by dynamically adjusting to uncertainties, maintaining a constant DC bus voltage, and optimizing battery usage. This advancement supports Sustainable Development Goals by promoting affordable, reliable, and sustainable energy access (SDG 7), fostering innovation (SDG 9), and mitigating climate change impacts (SDG 13). The study underscores the potential of adaptive control strategies in advancing microgrid technologies for sustainable development.

References

References are available upon request from the corresponding author.

Abbreviations

• SDG: Sustainable Development Goals
• DER: Distributed Energy Resources
• DC: Direct Current
• PI: Proportional-Integral
• MRAC: Model Reference Adaptive Controller
• PV: Photovoltaic
• WECS: Wind Energy Conversion System
• MPPT: Maximum Power Point Tracking
• BESS: Battery Energy Storage System
• THD: Total Harmonic Distortion

1. Sustainable Development Goals (SDGs) Addressed or Connected

1. SDG 7: Affordable and Clean Energy
   • The article focuses on developing efficient controllers for DC Microgrid systems integrating solar and wind energy, which are renewable energy sources.
   • It emphasizes optimal power flow management, maximum energy harvesting from solar and wind, and stable voltage regulation, contributing to clean and affordable energy access.
2. SDG 9: Industry, Innovation and Infrastructure
   • The research involves innovative control strategies (Model Reference Adaptive Controller) for microgrid systems enhancing resilience and stability.
   • It supports the development of sustainable infrastructure and promotes innovation in energy management systems.
3. SDG 13: Climate Action
   • By promoting renewable energy integration and reducing dependency on fossil fuels, the article contributes to climate change mitigation.
   • Efficient management of renewable resources in microgrids supports reduction of greenhouse gas emissions.
4. SDG 11: Sustainable Cities and Communities
   • The microgrid system can provide reliable electricity to rural and urban areas, improving energy access and sustainability of communities.

2. Specific Targets Under the Identified SDGs

1. SDG 7: Affordable and Clean Energy
   • Target 7.1: By 2030, ensure universal access to affordable, reliable and modern energy services.
   • Target 7.2: Increase substantially the share of renewable energy in the global energy mix.
   • Target 7.3: Double the global rate of improvement in energy efficiency.
2. SDG 9: Industry, Innovation and Infrastructure
   • Target 9.4: Upgrade infrastructure and retrofit industries to make them sustainable, with increased resource-use efficiency and greater adoption of clean and environmentally sound technologies.
   • Target 9.5: Enhance scientific research, upgrade technological capabilities of industrial sectors, including energy systems.
3. SDG 13: Climate Action
   • Target 13.2: Integrate climate change measures into national policies, strategies and planning.
4. SDG 11: Sustainable Cities and Communities
   • Target 11.1: Ensure access for all to adequate, safe and affordable housing and basic services, including reliable energy.

3. Indicators Mentioned or Implied to Measure Progress

1. Energy Generation and Efficiency Indicators
   • Power output from photovoltaic (PV) systems and wind energy conversion systems (WECS) measured in kilowatts (kW) and watts (W).
   • Voltage regulation at the DC bus (maintaining 48 V constant voltage) as a measure of system stability and efficiency.
   • Battery state-of-charge (SOC) and current (charging/discharging) as indicators of energy storage management.
   • Total Harmonic Distortion (THD) values of grid voltage to assess power quality (e.g., THD of 0.87 for PI controller and 0.26 for adaptive PI controller).
2. Control System Performance Indicators
   • Error indices in voltage regulation (e.g., 30-40% error with PI controller reduced by Model Reference Adaptive Controller).
   • Tracking error between reference and actual current in adaptive control systems (3-4% difference).
   • Power balance equations and power flow management cases indicating system reliability and resilience.
3. Renewable Resource Input Indicators
   • Solar irradiance levels (W/m²) and wind speed (m/s) data used as real-time inputs to evaluate system performance.

4. Table of SDGs, Targets, and Indicators

Source: nature.com