Report on Sustainable and Economic Operation of Multi-Energy Microgrids Incorporating Plug-in Hybrid Electric Vehicles (PHEVs)

Abstract

The integration of Plug-in Hybrid Electric Vehicles (PHEVs) into distribution networks, particularly microgrid (MG) systems, is essential for reducing costs and environmental impacts. Demand-side management (DSM) strategies such as load shifting and price-based demand response (PBDR) enhance the economic performance of MGs. This report presents a novel one-to-one-based optimization algorithm (OOBO) aimed at minimizing the generation cost of MG systems while considering PHEV operational factors and DSM tactics. The MG system operates in grid-connected mode, powered by microturbines, fuel cells, and renewable energy sources, supporting PHEV charging. Three PHEV types with varying battery capacities and charging/discharging power levels are analyzed. A balanced economic emission dispatch (BEED) approach optimizes the trade-off between cost minimization and pollution reduction, ensuring sustainable MG operation. Results demonstrate improved load factors and reduced peak demand through DSM, with early-hour PHEV charging proving cost-effective due to lower load and electricity prices. The approach promotes economic efficiency and greener energy use, aligning with Sustainable Development Goals (SDGs), particularly SDG 7 (Affordable and Clean Energy).

Introduction

Integrating PHEVs and battery storage into grid-connected microgrids enhances sustainability and economic efficiency in real-time distribution networks. PHEVs reduce greenhouse gas emissions by shifting energy reliance from fossil fuels to cleaner electricity, supporting SDG 7. When combined with renewable energy sources (solar, wind), PHEVs and battery storage aid in load balancing and energy arbitrage, improving grid stability and maximizing green energy utilization. Batteries increase resilience by storing surplus energy for peak demand periods, reducing pressure on traditional power plants.

Economically, PHEVs and battery storage facilitate demand-side management and peak shaving, lowering electricity costs by reducing reliance on expensive grid power during peak hours. Vehicle-to-grid (V2G) technology allows PHEVs to act as storage units, selling power back to the grid and providing financial incentives to owners. This integration fosters a robust, cost-efficient, and environmentally friendly power distribution system, contributing to SDG 7 and SDG 13 (Climate Action).

Literature Review

Recent studies highlight the advantages and challenges of renewable energy sources (RES) and battery energy storage systems (BESS) in microgrids, focusing on optimization, degradation modeling, and demand response integration. Various optimization algorithms and scheduling strategies have been proposed to minimize costs and emissions while enhancing system reliability and user satisfaction. The integration of DSM and PHEVs is recognized as a key factor in achieving sustainable and economic microgrid operation, supporting multiple SDGs including SDG 7 and SDG 13.

Research Gaps and Novelty

1. Balanced Demand and Supply: This study emphasizes a sophisticated energy management system that balances economic efficiency and environmental impact through normalized multi-objective optimization.
2. Optimization Method: The OOBO algorithm is introduced for its efficiency and flexibility in solving complex multi-objective optimization problems in ME-MGs.
3. Uncertainty Modeling: Incorporation of uncertainties in renewable generation and load demand using the two-point estimation method enhances the robustness of the energy management system.
4. DSM and PHEV Integration: The study analyzes how electric vehicles, via DSM load shifting and curtailment strategies, improve energy efficiency and reduce operational costs, promoting sustainable energy use.

Formulation of Fitness Functions

Charging and Discharging of PHEVs

PHEV charging demand is modeled using probability density functions, considering controlled, uncontrolled, and smart charging patterns. Smart charging aligns with periods of surplus grid energy or low electricity prices, aiming to reduce peak load and operational costs, supporting SDG 7.

Renewable Energy Output Modeling

• Wind Energy: Wind turbine output is modeled based on wind speed variability using Weibull distribution functions.
• Photovoltaic (PV) Energy: PV output depends on solar irradiance, modeled using beta distribution functions to capture stochastic behavior.

Objective Function

The microgrid cost function includes generation costs, grid energy transactions, and PHEV charging costs. Emission costs are calculated for fossil fuel-based units and grid emissions. A balanced economic emission dispatch (BEED) approach optimizes a weighted sum of normalized cost and emission functions, aligning with SDG 7 and SDG 13.

Demand-Side Management Policies

• Optimal Load Shifting Model (OLSM): Aims to reduce peak demand and energy costs by shifting elastic loads to off-peak hours.
• Incentive-Based Demand Response (IBDR): Encourages consumer participation through incentives, adjusting load based on price elasticity.
• Price-Based Demand Response (PBDR): Models consumer load responsiveness to electricity price fluctuations, promoting efficient energy use.
• Order Characteristics Load Shifting Policy (OCLSP): Rearranges load demand profiles to reduce peak demand and improve load factor without changing total energy consumption.

One-to-One-Based Optimization Algorithm (OOBO)

OOBO is a metaheuristic algorithm designed to generate and iteratively improve multiple candidate solutions within the search space, avoiding premature convergence. The algorithm updates population members’ positions based on fitness evaluations to find quasi-optimal solutions efficiently. OOBO is applied to optimize the multi-objective energy management problem in ME-MGs, balancing cost and emission objectives.

Results and Discussion

System Description

The studied ME-MG includes distributed energy resources (DERs) such as fuel cells, gas microturbines, photovoltaic systems, wind turbines, electric vehicles, and combined heat and power systems. The system operates in grid-connected mode, with a peak demand of 90 kW and daily consumption of 1695 kWh. PHEVs with varying battery capacities and state-of-charge (SOC) parameters are integrated.

Case Studies and Analysis

1. Base Case: OOBO minimized generation costs for three PHEV types, demonstrating effective integration of PHEVs into the MG load profile.
2. Grid Participation Modes:
   • Passive grid mode increased generation costs due to no power buyback.
   • Infinite grid mode reduced costs by allowing power transactions.
3. Optimal Load Shifting Model (OLSM): Reduced peak demand and generation costs by shifting elastic loads to low-price hours, improving load factor and economic efficiency.
4. Order Characteristics Load Shifting Policy (OCLSP): Achieved peak demand reduction and load factor improvement without optimization algorithms, demonstrating a simple yet effective DSM strategy.
5. Price-Based Demand Response (PBDR): Implemented critical peak pricing (CPP) to reshape load demand, resulting in significant energy savings and cost reductions.
6. Balanced Cost and Emission Dispatch: OOBO optimized a weighted objective balancing generation cost and emissions, achieving sustainable operation with reduced environmental impact.
7. Sensitivity Analysis: Variations in PHEV parameters (arrival/departure SOC and timing) showed impacts on generation costs, highlighting the importance of smart charging strategies.
8. Algorithm Performance: Statistical analysis confirmed OOBO’s superiority in minimizing generation costs with robustness and computational efficiency compared to other metaheuristic algorithms.

Conclusion

Multi-energy microgrids integrating PHEVs with renewable and distributed generation resources effectively reduce operational costs and environmental emissions, enhancing system efficiency and reliability. The OOBO algorithm demonstrates high performance in optimizing energy management, balancing economic and environmental objectives. Demand-side management strategies, including load shifting and price-based demand response, significantly improve load profiles and reduce peak demand, contributing to SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action).

1. Optimal load shifting (OLSM) achieved up to 4% generation cost reduction compared to the base case, outperforming OCLSP.
2. Critical peak pricing (CPP) in PBDR realized energy savings of 185 kWh and cost savings of $185.
3. A normalized multi-objective optimization approach with equal weighting effectively balanced cost and emission objectives.

Limitations and Future Work

While the study advances energy management in ME-MGs, limitations include assumptions on PHEV availability, DSM participation, and computational complexity affecting real-time scalability. Future research should focus on enhancing model robustness, scalability, and integration of diverse energy storage technologies (hydrogen, battery, gravitational) to accommodate increasing PHEV adoption and operational uncertainties.

Alignment with Sustainable Development Goals (SDGs)

• SDG 7 – Affordable and Clean Energy: The integration of PHEVs and renewable energy in microgrids promotes access to sustainable and reliable energy, reduces fossil fuel dependence, and lowers energy costs.
• SDG 13 – Climate Action: Balanced economic emission dispatch and DSM strategies reduce greenhouse gas emissions and environmental pollution.
• SDG 9 – Industry, Innovation, and Infrastructure: The development and application of advanced optimization algorithms (OOBO) and smart grid technologies foster innovation in energy infrastructure.
• SDG 11 – Sustainable Cities and Communities: Efficient microgrid operation supports resilient and sustainable urban energy systems.

1. Sustainable Development Goals (SDGs) Addressed

1. SDG 7: Affordable and Clean Energy
   • The article discusses integration of plug-in hybrid electric vehicles (PHEVs), renewable energy sources (solar, wind), and microgrids to provide sustainable, reliable, and affordable energy.
   • Focus on reducing greenhouse gas emissions and improving energy efficiency aligns with SDG 7 objectives.
2. SDG 13: Climate Action
   • Reduction of greenhouse gas emissions through balanced economic emission dispatch and use of cleaner energy sources supports climate action goals.
   • Demand-side management (DSM) and optimization algorithms contribute to lowering environmental impacts.
3. SDG 9: Industry, Innovation and Infrastructure
   • Development and application of novel optimization algorithms (One-to-One-Based Optimization – OOBO) for microgrid energy management indicate innovation in infrastructure.
   • Integration of smart charging and demand response strategies promotes sustainable industrial and infrastructure development.
4. SDG 11: Sustainable Cities and Communities
   • Microgrids and PHEVs contribute to sustainable urban energy systems by improving energy efficiency and reducing pollution.

2. Specific Targets Under the Identified SDGs

1. SDG 7 Targets
   • 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.
   • Target 7.a: Enhance international cooperation to facilitate access to clean energy research and technology.
2. SDG 13 Targets
   • Target 13.2: Integrate climate change measures into national policies, strategies and planning.
   • Target 13.3: Improve education, awareness-raising and human and institutional capacity on climate change mitigation.
3. SDG 9 Targets
   • Target 9.4: Upgrade infrastructure and retrofit industries to make them sustainable, with increased resource-use efficiency and greater adoption of clean technologies.
   • Target 9.5: Enhance scientific research and upgrade the technological capabilities of industrial sectors.
4. SDG 11 Targets
   • Target 11.6: Reduce the adverse per capita environmental impact of cities, including by paying special attention to air quality and municipal and other waste management.

3. Indicators Mentioned or Implied to Measure Progress

1. Energy Access and Efficiency Indicators (SDG 7)
   • Reduction in generation cost of microgrid systems ($ values reported in cases).
   • Load factor improvement (e.g., from 0.785 to 0.8 after load shifting).
   • Peak demand reduction (e.g., peak load reduced from 90 kW to 88 kW).
   • Share of renewable energy in microgrid supply (output power of DERs such as wind turbines and photovoltaics).
2. Emission Reduction Indicators (SDG 13)
   • Quantification of greenhouse gas emissions (e.g., CO₂ emissions in kg, SO₂, NOₓ coefficients).
   • Balanced economic emission dispatch values showing trade-offs between cost and emissions.
3. Innovation and Infrastructure Indicators (SDG 9)
   • Performance metrics of optimization algorithms (e.g., generation cost minimization, execution time, robustness, convergence curves).
   • Implementation of demand response programs and smart charging strategies.
4. Urban Sustainability Indicators (SDG 11)
   • Reduction in peak load and improved load management contributing to reduced environmental impact in communities.

4. Table of SDGs, Targets, and Indicators

Source: nature.com