Short answer

Implement predictive energy management systems for EV charging to dynamically balance renewable energy generation, grid supply, and charging demand, thereby minimizing costs and environmental impact.

Field: Resource Management
Source: IEEE Transactions on Industrial Informatics (2016)
Method: Simulation and Optimization Modelling
Evidence: Strong effect

Integrating photovoltaic power forecasting with an optimization algorithm for electric vehicle charging at the workplace can significantly reduce electricity costs and grid reliance. This resource management research insight is drawn from a 2016 study published in IEEE Transactions on Industrial Informatics. Using Simulation and optimization modelling, researchers explored how this design variable affects real-world outcomes. The key design takeaway: Implement predictive energy management systems for EV charging to dynamically balance renewable energy generation, grid supply, and charging demand, thereby minimizing costs and environmental impact.

Study

Resource ManagementHigh ImpactStrong effect

Workplace EV charging cost reduced by 427% using PV forecasting and optimized energy management

Integrating photovoltaic power forecasting with an optimization algorithm for electric vehicle charging at the workplace can significantly reduce electricity costs and grid reliance.

IEEE Transactions on Industrial Informatics · 2016

Key Findings

01The developed EMS significantly reduces EV charging costs compared to uncontrolled charging.
02The EMS increases PV self-consumption and reduces overall grid energy demand.
03Cost reductions of up to 427.45% were observed with two charging points under dynamic tariffs.

Application

Design takeaway

Implement predictive energy management systems for EV charging to dynamically balance renewable energy generation, grid supply, and charging demand, thereby minimizing costs and environmental impact.

How to apply

When designing or specifying EV charging solutions for commercial or industrial sites with solar installations, incorporate an EMS that forecasts PV output and optimizes charging schedules based on real-time electricity tariffs.

Project actions

01When designing an EV charging system, consider how to integrate renewable energy sources.
02Explore how software can optimize charging times to take advantage of lower electricity prices or higher solar generation.

Method & Evidence

AimHow can an energy management system utilizing photovoltaic power forecasting and optimization algorithms reduce workplace electric vehicle charging costs and grid energy consumption?

MethodSimulation and Optimization Modelling

ProcedureDeveloped an energy management system (EMS) comprising a PV power forecasting model (ARIMA) and an optimization framework (mixed-integer linear programming). This system was simulated to manage power flow between PV, grid, and BEVs, aiming to minimize charging costs and maximize PV self-consumption under dynamic tariff conditions.

ContextWorkplace electric vehicle charging infrastructure

Variables

IV["Presence and sophistication of the energy management system (uncontrolled vs. optimized)","PV power generation forecast accuracy","Electricity tariff structure (dynamic vs. static)"]

DV["Total EV charging cost","Percentage of PV self-consumption","Grid energy consumption"]

CV["Number of charging points","BEV battery capacity and charging rate","PV system size","Duration of charging period"]

Strengths & Limitations

Strengths

+Addresses a relevant and growing problem of EV fleet management.
+Utilizes established modelling techniques (ARIMA, MILP) for forecasting and optimization.

Limitations

The accuracy of PV power forecasts and the complexity of real-time tariff structures can affect the real-world performance of such systems.

Reliability & validity

The reliability of the findings depends on the accuracy of the ARIMA model for PV forecasting and the robustness of the MILP optimization under various simulated conditions. Validity is supported by comparing results against a baseline uncontrolled charging policy.

Think critically

To what extent can the savings observed in simulation be replicated in a real-world deployment, considering factors like battery degradation, grid stability, and unpredictable user behaviour?

Design Principles

"Intelligent energy management systems should leverage forecasting and optimization to align energy consumption with available renewable resources and dynamic pricing."

This approach allows organizations to leverage renewable energy sources more effectively, decreasing operational expenses associated with EV fleets. It also contributes to a more sustainable energy infrastructure by maximizing self-consumption of generated solar power.

What This Means for Your Design

Using smart technology to predict sunshine and electricity prices can make charging electric cars at work much cheaper and greener.

How to use in your project

1.Reference this study when discussing the economic and environmental benefits of optimized EV charging solutions.
2.Use the findings to justify the inclusion of energy management systems in your design proposal.

Add to My Project

Quick Cite

Paragraph starter

The integration of photovoltaic power forecasting with optimized energy management systems, as demonstrated by van der Meer et al. (2016), offers a robust strategy for reducing electric vehicle charging costs at the workplace. Their research highlights potential cost reductions of over 400% by intelligently managing power flow between solar generation, the grid, and vehicle batteries, thereby increasing self-consumption and decreasing reliance on grid electricity.

Source

IEEE Transactions on Industrial Informatics

Energy Management System With PV Power Forecast to Optimally Charge EVs at the Workplace

journal · 2016

View source

Questions About This Research

What does the research say about workplace ev charging cost reduced by 427% using pv forecasting and optimized energy management?: Implement predictive energy management systems for EV charging to dynamically balance renewable energy generation, grid supply, and charging demand, thereby minimizing costs and environmental impact. Evidence: IEEE Transactions on Industrial Informatics (2016).
Why does "Workplace EV charging cost reduced by 427% using PV forecasting and optimized energy management" matter for design?: This approach allows organizations to leverage renewable energy sources more effectively, decreasing operational expenses associated with EV fleets. It also contributes to a more sustainable energy infrastructure by maximizing self-consumption of generated solar power.
How can designers apply this research?: Implement predictive energy management systems for EV charging to dynamically balance renewable energy generation, grid supply, and charging demand, thereby minimizing costs and environmental impact.
What were the main findings?: The developed EMS significantly reduces EV charging costs compared to uncontrolled charging.. The EMS increases PV self-consumption and reduces overall grid energy demand.. Cost reductions of up to 427.45% were observed with two charging points under dynamic tariffs.
What research method was used?: Simulation and Optimization Modelling.
How strong is the evidence?: Evidence strength is rated Strong effect, based on a 2016 journal from IEEE Transactions on Industrial Informatics.
What should I do differently in my next project?: When designing or specifying EV charging solutions for commercial or industrial sites with solar installations, incorporate an EMS that forecasts PV output and optimizes charging schedules based on real-time electricity tariffs.
What are the limitations?: The study's findings are based on simulation and may vary with real-world system complexities, actual weather patterns, and user charging behaviour.