Literature Review: Optimal sizing of hybrid energy systems in presence of electric vehicles

Notes to Reader[edit | edit source]

Dear reader, this topics reviews the literature in the context of optimal sizing of hybrid energy systems (HES) interconnected with electric vehicles (EVs). Please fill free to add more information or reviews to it.

Background[edit | edit source]

Search Strategy & Terms[edit | edit source]

Key Words[edit | edit source]

Optimization

Microgrids

Hybrid Energy Systems (HES)

Hybrid Renewable Energy Systems (HRES)

Energy Management Strategy (EMS)

Electric Vehicles (EVs)

Search Strategy[edit | edit source]

Google & Google Scholar

Topic[edit | edit source]

Optimal sizing of hybrid energy systems in presence of electric vehicles

Theoretical Framework[edit | edit source]

1. Renewable Energy System Design and Optimization[edit | edit source]

• Energy Systems Theory: This theory helps understand the flow and transformation of energy within HES and provides guidelines for integrating multiple energy sources, including solar photovoltaic (PV), wind, and battery storage. It emphasizes energy efficiency, reliability, and sustainability.^[1]
• Optimization Theory: Multi-objective optimization, particularly using algorithms like Particle Swarm Optimization (PSO) ^[1] and the Turbulent Flow of Water Optimization (TFWO),^[2] provides a methodological basis for minimizing system costs, maximizing reliability, and efficiently managing resources under stochastic conditions.^[3]

2. Vehicle-to-Grid (V2G), Grid-to-Vehicle (G2V), Vehicle-to-Home (V2H) and Home-to-Vehicle (H2V) Concepts[edit | edit source]

• V2G and G2V: This concept considers EVs as mobile energy storage, where they can supply energy to the grid or absorb excess energy. The V2G model supports grid stability by mitigating demand fluctuations, while G2V enables optimal charging strategies that align with grid needs. ^[2][4][5]
• V2H and H2V: V2H and H2V concepts integrate EVs and home energy systems to enhance resilience and efficiency. V2H enables EVs to power residential loads, boosting energy autonomy and sustainability, especially during outages. H2V uses home energy for EV charging, optimized for times of peak renewable output, supporting both cost savings and HES efficiency .^[5]
• Demand Response: This focuses on modifying energy consumption patterns to align with availability and grid capacity. By incorporating EV charging and discharging schedules, demand response supports efficient HES operation, especially in peak demand management.^[6]

3. Stochastic Modeling and Uncertainty Analysis[edit | edit source]

• Stochastic Processes and Monte Carlo Simulation: These methods address uncertainties in renewable energy outputs (e.g., solar and wind) and EV usage patterns. Modeling randomness in user behavior and environmental conditions enables more robust system configurations and sizing.^[7][8][9]
• Reliability Theory: This theory underpins the concept of Loss of Power Supply Probability (LPSP) in HES , addressing energy system reliability and ensuring demand satisfaction by minimizing interruptions.^{[1][8][9][10]}

4. Economic and Environmental Theory[edit | edit source]

• Life Cycle Cost (LCC) Analysis: LCC theory supports economic evaluation by calculating total costs over the system's lifecycle. This helps balance initial investment with long-term operational costs in the optimization of HES.^{[1][11][12][10]}
• Sustainable Development Theory: Anchored in environmental responsibility, this theory emphasizes the need for renewable integration and the reduction of reliance on fossil fuels, framing the rationale for HES and EVs as steps toward lower emissions and environmental impact.^[13][14]

5. Energy Management Theory[edit | edit source]

• Energy Management Systems (EMS): A foundation for EMS, enabling dynamic management of energy flows in HES. EMS incorporates real-time data to control generation, storage, and EV charging, ensuring that the system maintains balance and minimizes cost.^[1][7]

Significance and Importance[edit | edit source]

Studies in this context are significant as they explore integrating EVs with HES V2H, H2V, V2G, and G2V technologies. By optimizing these interactions, studies address key challenges in energy resilience, cost-effectiveness, and sustainability.

Current State of the Art[edit | edit source]

• Potential for EVs to act as mobile storage units, reducing dependency on conventional power and enhancing grid stability, especially in isolated or under-resourced areas.

Current advancements in integrating EVs with HES focus on smart energy management, predictive modeling, and bidirectional power flow (V2G and V2H) for efficient, resilient systems. Optimization algorithms balance power between EVs, renewables, and the grid, while coordinated charging (H2V) maximizes renewable usage. These developments support cost-effective, flexible energy systems for residential and isolated microgrids, promoting a renewable-centered energy future.

Relevant Stakeholders[edit | edit source]

• Researchers
• Findings could inform policy and promote energy autonomy, further supporting renewable energy adoption and efficient energy management.

Applicability and Context[edit | edit source]

• Academia
• Industry
• Community
• Personal

Literature[edit | edit source]

Day-ahead resource scheduling in smart grids considering vehicle-to-grid and network constraints^[15][edit | edit source]

• Day-Ahead Scheduling with V2G: This study introduces a day-ahead energy scheduling approach for smart grids, incorporating V2G and distributed generation. It highlights the importance of efficient energy management when EVs are integrated, relevant to your work on optimizing hybrid energy systems with EVs.
• Role of Virtual Power Players (VPP): The concept of a VPP managing resources, including contracts with V2G owners, mirrors your focus on optimizing energy flows and decision-making within hybrid systems. It also provides insights into the benefits of contractual coordination with EV owners to enhance energy flexibility.
• AC Power Flow for Network Constraints: The study's inclusion of full AC power flow analysis for network constraints—such as thermal and voltage limits—is useful for understanding the operational and technical challenges in smart grid setups, which could inform constraint handling in your model.
• Multi-Day Influence on Scheduling: This paper demonstrates the impact of multi-day requirements on optimal scheduling, showing that considering successive days improves battery management efficiency and system feasibility. This is especially relevant to long-term sustainability in hybrid energy systems.

Optimal charging of electric drive vehicles in a market environment^[16][edit | edit source]

• EV Flexibility in Energy Markets: This paper presents a framework for optimizing EV charging and discharging from an aggregator’s perspective, focusing on the flexibility EVs can offer through charging based on market prices. This aligns well with your study on using EVs within hybrid systems for economic optimization.
• Market-Based Optimization Models: The framework differentiates between linear programming for price-taker scenarios and quadratic programming for aggregators with market power, offering insights into different optimization approaches that may be useful for optimizing hybrid systems with market considerations.
• Case Study on Driving Patterns and Market Prices: The Danish case study illustrates how driving behavior and electricity price variations impact optimal EV charging/discharging strategies, which could inform the variability and behavioral aspects in your system modeling.
• Flexibility Constraints: The paper finds that EV flexibility is mostly within the same day, limited by fixed driving patterns, emphasizing the need to account for usage restrictions and short-term flexibility constraints—relevant for realistic modeling of EVs in your hybrid system.

Optimal scheduling of renewable micro-grids considering plug-in hybrid electric vehicle charging demand^[7][edit | edit source]

• Stochastic Framework for MGs with RESs and PHEVs: This paper presents a stochastic framework using Monte Carlo simulations to account for uncertainties in renewable sources and PHEV usage. This aligns with your focus on incorporating EV-related uncertainties in microgrid optimization.
• Charging Patterns for PHEVs: Examines three charging schemes—uncontrolled, controlled, and smart charging—showing that smart charging significantly reduces overall microgrid costs, an insight relevant for evaluating EV charging strategies in your hybrid system.
• SOS Optimization Algorithm: Utilizes the Symbiotic Organisms Search (SOS) algorithm, with a modified version enhancing local and global search capabilities, to optimize microgrid operations. The advanced algorithm's success in minimizing costs may provide guidance for optimization methods in your study.
• Cost Impact of PHEV Integration: Highlights that while PHEV demand can raise microgrid costs, smart charging mitigates this effect, reinforcing the cost-saving potential of smart charging strategies in EV-integrated hybrid systems.
• Comparison of MG Case Studies: Applies the framework to two microgrid test systems, validating the model’s effectiveness across different setups—useful for understanding adaptable approaches to various microgrid configurations in your research.

Coordinated stochastic optimal energy management of grid-connected microgrids considering demand response, plug-in hybrid electric vehicles, and smart transformers^[3][edit | edit source]

• Coordinated Operation of a Grid-Connected Microgrid: This study optimizes a grid-connected AC microgrid with both controllable and uncontrollable sources, battery storage, PHEVs, and demand response programs, paralleling your exploration of hybrid energy systems integrated with EVs.
• Stochastic Modeling of Uncertainties: Uses “Hong’s 2m point estimate method” to model uncertainties in renewable generation, EV load, demand, and grid prices, addressing a core need in your work to manage variability in hybrid systems.
• Demand Response Program and Incentives: Includes an incentive-based demand response program (IBDR) to minimize costs and support optimal economic operation, providing insights into incentivizing EV charging behavior in your system.
• Smart Transformer for Voltage Control: A smart transformer improves voltage control and reduces network loss in coordination with the energy management scheme, which could inform voltage control strategies in your study.
• Charging Strategies for PHEVs: Studies multiple PHEV charging strategies, finding that smart charging reduces costs and enhances grid efficiency—supportive of similar analyses in your project.
• Simulation on 33-Bus System: The framework's validation on a standard test system demonstrates its practical feasibility, useful for benchmarking your hybrid system's performance.

Cooperative energy scheduling of interconnected microgrid system considering renewable energy resources and electric vehicles^[17][edit | edit source]

• Techno-Sustainable-Economic Framework for Microgrids: Presents a framework focusing on multi-objective optimization (cost, power loss, carbon emissions), which aligns well with optimizing hybrid energy systems (HES) integrated with EVs in your study.
• Cooperative Framework for Microgrid Systems: Proposes microgrid cooperation through local energy trading to reduce costs, power losses, and emissions—offering insights into potential collaboration and resource-sharing strategies for your HES model.
• Cost Allocation Using Shapley Value: Applies the Shapley value for fair cost distribution among microgrids, which could be useful for cost-sharing or benefit analysis when integrating EVs and renewable resources in a community or shared energy setup.
• EV Impact on Power Loss and Cost Reduction: Sensitivity analysis includes EVs’ effects on power losses and cost, relevant to your exploration of EVs within HES for cost-efficient energy scheduling.
• Simulation on IEEE 33-Bus Test System: Tested on a 33-bus system, showcasing reductions in power losses (68.67%) and emissions (19.65%)—potential performance benchmarks for your HES under similar scenarios.

Optimal planning and design of integrated energy systems in a microgrid incorporating electric vehicles and fuel cell system^[18][edit | edit source]

• Stochastic Optimization for PEV Management: This model addresses the challenges of PEV integration into the grid by managing charging behaviors, which could provide strategies for incorporating EVs in your HES for optimal load and cost management.
• V2G as Cost-Saving: By treating EVs as mobile storage through V2G, the system achieves lower operational costs, aligning with your aim to explore the economic advantages of EVs in a hybrid energy system.
• Modified Fluid Search Optimization (MFSO): MFSO proves effective for optimizing microgrid operations with faster computation times than GA and PSO, offering a promising approach for your energy scheduling model where efficient computation is critical.
• Renewable Uncertainty Management: Incorporates uncertainty of PV and wind generation, directly relevant for managing the stochastic behavior of renewables in your PV-WT-Battery system.
• High Renewable Penetration Impact: Demonstrates that increased renewable energy affects MG operations, reinforcing your objective to assess the implications of high renewable and EV integration in residential settings.

Designing smart hybrid renewable energy systems with V2G^[12][edit | edit source]

• Life-Cycle Cost Optimization: The system’s life-cycle cost, specifically evaluated as the objective function, is minimized to achieve optimal configuration. This aligns with your interest in economic optimization for hybrid energy solutions.
• Optimization Decision Variables: Key decision variables include the type and capacity of solar PV panels, wind turbines, and V2G charging center size—relevant to identifying optimal HES configurations that consider renewable resource types and storage capacities.
• Impact of Grid Cost: The study performs sensitivity analysis to understand the impact of grid costs on system configuration and energy flow

Optimal sizing of hybrid renewable energy systems in presence of electric vehicles using multi-objective particle swarm optimization^[8][edit | edit source]

• Objective:
  • This paper investigates the optimal sizing of microgrid resources with EVs using a multi-objective particle swarm optimization (MOPSO) algorithm. It models EV uncertainties via Monte Carlo Simulation.
• Design Comparison:
  • Two configurations are analyzed:
    • PV/wind/battery
    • PV/wind/battery/EV
  • Findings show that adding EV after initial optimization (PV/wind/battery) is more cost-effective than designing with EV initially, due to reduced wind units needed for identical LPSP targets.
• Reliability Enhancement:
  • EVs increase MG reliability in both deterministic and stochastic behavior cases, reducing LPSP values.
• Sensitivity Analysis:
  • The impact of wind speed and load demand variations (+/- 10%, 20%, 30%) on MG performance in the PV/wind/battery/EV design is assessed, showing these parameters significantly affect decision variables.
• Future Directions:
  • Suggestions include adding diesel generators, fuel cells, expanding to thermal energy needs, modeling wind/PV output uncertainties, and exploring alternative optimization algorithms.

Optimal sizing of hybrid renewable energy systems by considering power sharing and electric vehicles^[9][edit | edit source]

• Objective:
  • This study focuses on optimizing the sizing of HRES with power-sharing and EV integration. Two case studies (CS1 and CS2) are examined with and without EVs, using MOPSO and multi-objective crow search (MOCS).
• Case Studies:
  • CS1: Four HRES setups include EVs with power-sharing capability, simulating EV stochastic behavior with Monte Carlo Simulation.
  • CS2: Similar HRES setups without EVs. Results show EV integration in CS1 reduces total system cost.
• Optimization Findings:
  • MOPSO outperformed MOCS, achieving lower LPSPs (0.67 in CS1, 0.91 in CS2), indicating higher system reliability with EV inclusion.
• Power Sharing:
  • Power-sharing behavior among HRESs is analyzed over 24 hours, with the system efficiently purchasing and selling power as required.
• Results and Sensitivity:
  • EV integration significantly reduces lifecycle costs and improves reliability. Higher energy waste occurs midday.
• Future Work:
  • Suggested improvements include adding demand response models, testing under various climates, evaluating grid-connected HRES designs, and exploring alternative optimization methods and renewable resources.

Optimal sizing of an isolated microgrid with electric vehicles using stochastic programming^[10][edit | edit source]

• Objective:
  • This study optimizes the configuration of an isolated microgrid with renewable sources (wind and PV), batteries, and EVs to minimize LCC and increase reliability.
• Optimization Approach:
  • A two-stage stochastic programming model is used to handle renewable and EV uncertainties, creating annual scenarios to capture seasonal variation. The model uses MILP to solve the multi-objective problem, balancing LCC and LPSP.
• Key Findings:
  • A slight LPSP increase reduces costs by 10-20% in each EV scenario.
  • Efficient EV charge/discharge systems significantly reduce total cost, lowering battery needs by around 3%.
  • Larger chargers decrease system costs further, with a doubled charger size cutting battery count by 3%.
• EV Role:
  • Using EVs as storage lowers the total system cost and reduces dependency on extra batteries.
  • Cases with high charging/discharging efficiency show the lowest costs, demonstrating EVs' potential as a flexible energy asset.
• Future Implications:
  • EV integration in microgrids can lower costs and improve reliability. The study's findings can guide policies supporting EV subsidies and highlight the benefits of EV usage for both isolated and grid-connected microgrids, promoting renewable energy and reducing market volatility.

Optimal sizing of photovoltaic/wind/battery hybrid renewable energy system including electric vehicles using improved search space reduction algorithm^[19][edit | edit source]

• Objective:
  • This study presents an optimal sizing methodology for a standalone PV/wind/battery HRES using an improved Search Space Reduction (SSR) algorithm, considering the impact of EVs on reliability and cost.
• Optimization Approach:
  • A novel SSR algorithm models EV stochastic behavior through probability distribution functions for arrival, departure, and SOC. Two scenarios assess the impact of EVs: one adds EVs after initial HRES sizing, and the other includes EVs during sizing.
• Findings:
  • Scenario 1: Adding EVs post-sizing reduced levelized cost of energy (LCE) by 6.85%.
  • Scenario 2: Including EVs in the sizing phase lowered LCE by 8%.
  • Increasing EV count from 0 to 500 reduced LPSP from 5% to 4.64% and lowered cost per kWh as fewer batteries were needed.
• Algorithm Comparison:
  • The improved SSR algorithm outperformed PSO, GA, and the original SSR, achieving optimal solutions in only 45 iterations.
• Conclusion:
  • Integrating EVs into HRES provides both reliability and economic benefits, as EVs reduce battery needs and overall energy costs, demonstrating the potential of home-parked EVs as a resource in standalone systems.

Optimal sizing of PV-BESS units for home energy management system-equipped households considering day-ahead load scheduling for demand response and self-consumption^[20][edit | edit source]

• Objective:
  • The study aims to optimize PV-BESS sizing for Home EMS-equipped prosumers, enhancing self-consumption and reducing electricity bills amid lower feed-in tariffs.
• Methodology:
  • The model uses MILP for demand response and self-consumption maximization, incorporating demand-side management (DSM), optimal PV tilt angle, load scheduling (for different appliances), battery degradation, and V2H technology. It determines the optimal configuration based on NPV.
• Case Study:
  • For a 37.5 kWh/day household in Istanbul, the optimal setup was a 3 kW PV (no BESS) at a 10° tilt. HEMS use increased NPV to $2273 compared to $920 without HEMS.
• Sensitivity Analysis:
  • BESS viability improves with rising electricity (+25%) or falling battery prices (-25%). Configurations range from 4 kW PV with 2.5 kWh BESS at -25% battery cost to 7 kW PV with 7.5 kWh BESS at +100% electricity prices.
• Conclusion:
  • Home EMS significantly enhances system feasibility. The model is versatile for application in various regions and could extend to grid-connected microgrids with P2P energy trading.

Integrating electric vehicles into hybrid microgrids: A stochastic approach to future-ready renewable energy solutions and management^[2][edit | edit source]

• Objective:
  • This study explores integrating EVs into a hybrid microgrid (PV, WT, BESS, EV grid connection) and assesses the V2G and G2V models for optimizing grid pressure relief, energy cost, and sustainability.
• Optimization Approach:
  • A rule-based energy management scheme and various algorithms (e.g., TFWO, PSO, GA, WSO) were tested to determine the optimal configuration, focusing on annual cost, LPSP, and LCOE. The TFWO algorithm delivered optimal results with efficient convergence.
• Findings:
  • The optimized microgrid configuration met 69.87% of energy demand from renewables and achieved an LCOE of $0.1597/kWh. The system’s PV, WT, and BESS capacities were 411.0560 kW, 327.0229 kW, and 561.1750 kW, respectively.
• Sensitivity Analysis:
  • Changes in load and interest rates significantly impacted system performance, underscoring the need for careful planning around financial and demand factors.
• Conclusion:
  • The study demonstrates the TFWO algorithm’s efficiency and emphasizes EVs’ potential in HRES. It advocates for continued research on diverse energy infrastructures and advanced storage solutions, which can guide future sustainable energy strategies.

References[edit | edit source]