Reducing energy costs by using optimal electric vehicles scheduling and renewable energy sources

Javor, Dario; Janjić, Aleksandar; Raicevic, Nebojsa

doi:10.1109/infoteh.2019.8717760

Cited by 5 publications

(2 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, we consider the impact of a real‐time power generation‐demand imbalance. Two of the most uncertain emerging energy sources in microgrids are micro‐renewables and electric vehicles (EVs) [5, 6], whose dispatch is often challenging to plan [7], optimize [8] and control [9] because they depend on the weather (renewables) and human behaviour (EVs).…”

Section: Introductionmentioning

confidence: 99%

Tuning of renewable energy bids based on energy risk management: Enhanced microgrids with pareto‐optimal profits for the utility and prosumers

Mohan

Antonis

Jisma

et al. 2022

Energy Conversion and Econom

View full text Add to dashboard Cite

The increasing penetration of renewable energy sources (RES) and electric vehicles (EVs) demands the building of a microgrid energy portfolio that is cost‐effective and robust against generation uncertainties (energy risk). Energy risk may trigger financial risk in the local energy market, depending on bid values, cost of generation and price of upstream grid power. In this study, a microgrid energy portfolio is built based on adjustments to both the financial and energy risks. These risks are managed in two ways: (1) by pre‐tuning and prioritizing the bid prices for wind and solar energy sources based on their relative levels of energy risk as quantified through a conditional value‐at‐risk (CVaR) approach; and (2) by co‐optimizing the conflicting profits of the utility and prosumers using non‐dominated sorting particle swarm optimization (NSPSO) to obtain a risk‐adjusted Pareto‐optimal energy mix. Thus, the utility predicts the net power balancing cost from the scheduling time horizon, thereby moderating the adverse effect that the uncertainties in renewable energy could have on the collective welfare. The proposed method is tested on a grid‐connected CIGRE low‐voltage (LV) benchmark microgrid with solar and wind sources, microturbines, and EVs. The results demonstrate that the obtained portfolio is realistic, welfare‐optimized and cost‐efficient.

show abstract

Section: Introductionmentioning

confidence: 99%

Tuning of renewable energy bids based on energy risk management: Enhanced microgrids with pareto‐optimal profits for the utility and prosumers

Mohan

Antonis

Jisma

et al. 2022

Energy Conversion and Econom

View full text Add to dashboard Cite

show abstract

“…The authors of this paper considered a microgrid of one company having EVs fleet and its charging station with available V2G service, non-deferrable loads, PV panels, and wind turbines [12], [13]. Two-objective optimization problem was translated into single-objective optimization problem by weighted linear combination of two functions in [14], but battery degradation costs were not considered.…”

Section: Introductionmentioning

confidence: 99%

Multi-Criteria Optimization of Vehicle-to-Grid Service to Minimize Battery Degradation and Electricity Costs

Javor

Raicevic

Klimenta³

et al. 2022

ELEKTRON ELEKTROTECH

Self Cite

View full text Add to dashboard Cite

Increased use of renewable energy sources in energy sector as well as improvements and electrification in transportation sector significantly contribute to reduction of green-house gasses emissions and mitigation of problems with fossil fuel dependency. Optimal integration of electric vehicles (EVs) into the grids and their charging/discharging schedules have to be realized in accordance with electricity demand, day-ahead electricity market prices and intermittency of photovoltaic and wind generators electricity production. A microgrid that includes non-deferrable loads, renewable energy sources, EV fleet and its charging station is analyzed in this paper. Its Vehicle-to-Grid (V2G) service is optimized with the aim of minimizing the operational costs and obtaining peak load shaving and valley filling of the load curve, which is especially effective in the case of EVs fleet with occupational time intervals known in advance. Optimized schedule of EVs charging and discharging is obtained as a result of the procedure that uses multi-criteria optimization function. These criteria include minimization of microgrid electricity costs as the local aggregator’s benefit, maximization of the flattening of total microgrid demand curve as main grid operator’s benefit, and minimization of battery degradation (due to a number of charging/discharging cycles) as EVs owner’s benefit which is the novelty of this paper. Experimental analysis is performed on several scenarios and program Lingo is used to solve the optimization problem.

show abstract

A Novel algorithm for Electric Vehicle charging scheduling with Renewable Energy Source

Nethravathi

Venkatakirthiga

2021

IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society

View full text Add to dashboard Cite

Reducing energy costs by using optimal electric vehicles scheduling and renewable energy sources

Cited by 5 publications

References 6 publications

Tuning of renewable energy bids based on energy risk management: Enhanced microgrids with pareto‐optimal profits for the utility and prosumers

Tuning of renewable energy bids based on energy risk management: Enhanced microgrids with pareto‐optimal profits for the utility and prosumers

Multi-Criteria Optimization of Vehicle-to-Grid Service to Minimize Battery Degradation and Electricity Costs

A Novel algorithm for Electric Vehicle charging scheduling with Renewable Energy Source

Contact Info

Product

Resources

About