Day-Ahead Techno-Economic Optimization of a Grid-Connected VPP Considering BESS and EV Degradation Costs

This study investigates a day-ahead technoeconomic optimization framework for a grid-connected Virtual Power Plant (VPP) comprising electrical loads, photovoltaic (PV) generation, a battery energy storage system (BESS), and an electric vehicle charging station (EVCS). It addresses the complementary objectives of technical virtual power plants (TVPPs), which prioritize grid support and operational performance, and commercial virtual power plants (CVPPs), which focus on economic efficiency, by integrating both perspectives within a unified energy management framework. Day-ahead load and PV forecasts generated using long shortterm memory (LSTM) networks are incorporated into the optimization framework. The proposed approach accounts for BESS and EV degradation costs across different operating scenarios, enabling the assessment of their impacts on operating costs, peak grid demand, and techno-economic performance. The optimization framework evaluates three operatingscenarios: cost minimization, cost minimization subject to peakshaving constraint, and multi-objective optimization, whileaccounting for BESS and EV degradation costs for every case. The results indicate that incorporating degradation costs reduces net daily operating costs from €11,966 to €11,055 under cost minimization objective, from €11,648 to €11,522 subject to the peak-shaving constraint, and from €11,416 to €11,250 under multi-objective optimization. Peak grid power decreases from 5,880 to 4,230 kW under cost minimization and from 4,176 to 3,625 kW under multi-objective optimization. Both scenarios involving a peak-shaving constraint limit peak grid power to 3,150 kW, regardless of whether degradation costs are considered. These findings demonstrate that degradation-aware optimization improves the balance between net operating costs, battery lifetime,and peak-demand reduction, highlighting the value of integrating the complementary technical and economic objectives of TVPPs and CVPPs.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-11-03
DOI
https://doi.org/10.5281/zenodo.23926431
Primary Topic
Smart Grid Energy Management
Type
article
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article

Day-Ahead Techno-Economic Optimization of a Grid-Connected VPP Considering BESS and EV Degradation Costs

Basel Mokhtar El-Sayed
Zenodo (CERN European Organization for Nuclear Research)
Smart Grid Energy Management
article

Day-Ahead Techno-Economic Optimization of a Grid-Connected VPP Considering BESS and EV Degradation Costs

Basel Mokhtar El-Sayed
article en

Abstract

This study investigates a day-ahead technoeconomic optimization framework for a grid-connected Virtual Power Plant (VPP) comprising electrical loads, photovoltaic (PV) generation, a battery energy storage system (BESS), and an electric vehicle charging station (EVCS). It addresses the complementary objectives of technical virtual power plants (TVPPs), which prioritize grid support and operational performance, and commercial virtual power plants (CVPPs), which focus on economic efficiency, by integrating both perspectives within a unified energy management framework. Day-ahead load and PV forecasts generated using long shortterm memory (LSTM) networks are incorporated into the optimization framework. The proposed approach accounts for BESS and EV degradation costs across different operating scenarios, enabling the assessment of their impacts on operating costs, peak grid demand, and techno-economic performance. The optimization framework evaluates three operatingscenarios: cost minimization, cost minimization subject to peakshaving constraint, and multi-objective optimization, whileaccounting for BESS and EV degradation costs for every case. The results indicate that incorporating degradation costs reduces net daily operating costs from €11,966 to €11,055 under cost minimization objective, from €11,648 to €11,522 subject to the peak-shaving constraint, and from €11,416 to €11,250 under multi-objective optimization. Peak grid power decreases from 5,880 to 4,230 kW under cost minimization and from 4,176 to 3,625 kW under multi-objective optimization. Both scenarios involving a peak-shaving constraint limit peak grid power to 3,150 kW, regardless of whether degradation costs are considered. These findings demonstrate that degradation-aware optimization improves the balance between net operating costs, battery lifetime,and peak-demand reduction, highlighting the value of integrating the complementary technical and economic objectives of TVPPs and CVPPs.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 25%
Smart Grid Energy Management
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