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N.J.A. Steinmetz
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Vehicle-to-Grid (V2G) can provide flexibility to electricity systems, but its practical contribution to congestion mitigation depends not only on technical battery capacity, but also on user participation, vehicle availability, charging behaviour, and operational control. Existing research often treats participation as fixed or evaluates user acceptance separately from system performance. This study investigates which V2G system configurations are associated with lower local congestion pressure when participation emerges endogenously from heterogeneous contract preferences and behavioural feedback.
A full-year agent-based model of a stylised Dutch urban area was used to simulate the mobility, charging, contract acceptance, and V2G behaviour of 500 electric-vehicle drivers at five-minute resolution. Contract characteristics, charging policies, charging infrastructure, and renewable-energy conditions were varied across a large discrete design space. Congestion pressure was represented by a squared loss function penalising positive net-load exceedances above 600 kW. A learning-guided inverse-modelling procedure, based on a Bayesian Ridge surrogate and two independently seeded learning streams, directed simulations towards potentially favourable configurations. Each proposed configuration was evaluated through repeated stochastic simulations, and Patient Rule Induction Method was used to identify interpretable low-loss design regions.
The results show that participation is an enabling, but insufficient, condition for congestion relief. Higher participation was generally associated with lower loss, with the largest improvements occurring between low and medium-high participation levels and diminishing returns emerging at high participation. Guaranteed range was the strongest predictor of participation, followed by minimum plug-in time, while financial compensation and battery-degradation assumptions had smaller effects. Low congestion loss was most consistently associated with smart charging at residential locations, high renewable-energy coverage, and a medium-to-high guaranteed range. Smart charging and V2G performed complementary functions: controlled charging prevented avoidable EV demand from reinforcing existing peaks, while V2G reduced the remaining positive net load. Nevertheless, congestion remained concentrated in winter evenings, and a limited number of severe peaks persisted.
The inverse search identified favourable configurations relatively early and subsequently improved mainly by avoiding poor regions rather than converging towards a unique optimum. This study contributes by linking heterogeneous contract participation to realised, temporally available grid flexibility and by demonstrating how inverse modelling can identify multiple plausible socio-technical design regions. The findings indicate that effective V2G systems require the joint design of user contracts, charging control, renewable-energy conditions, and grid-responsive operation, rather than the maximisation of participation alone. ...
A full-year agent-based model of a stylised Dutch urban area was used to simulate the mobility, charging, contract acceptance, and V2G behaviour of 500 electric-vehicle drivers at five-minute resolution. Contract characteristics, charging policies, charging infrastructure, and renewable-energy conditions were varied across a large discrete design space. Congestion pressure was represented by a squared loss function penalising positive net-load exceedances above 600 kW. A learning-guided inverse-modelling procedure, based on a Bayesian Ridge surrogate and two independently seeded learning streams, directed simulations towards potentially favourable configurations. Each proposed configuration was evaluated through repeated stochastic simulations, and Patient Rule Induction Method was used to identify interpretable low-loss design regions.
The results show that participation is an enabling, but insufficient, condition for congestion relief. Higher participation was generally associated with lower loss, with the largest improvements occurring between low and medium-high participation levels and diminishing returns emerging at high participation. Guaranteed range was the strongest predictor of participation, followed by minimum plug-in time, while financial compensation and battery-degradation assumptions had smaller effects. Low congestion loss was most consistently associated with smart charging at residential locations, high renewable-energy coverage, and a medium-to-high guaranteed range. Smart charging and V2G performed complementary functions: controlled charging prevented avoidable EV demand from reinforcing existing peaks, while V2G reduced the remaining positive net load. Nevertheless, congestion remained concentrated in winter evenings, and a limited number of severe peaks persisted.
The inverse search identified favourable configurations relatively early and subsequently improved mainly by avoiding poor regions rather than converging towards a unique optimum. This study contributes by linking heterogeneous contract participation to realised, temporally available grid flexibility and by demonstrating how inverse modelling can identify multiple plausible socio-technical design regions. The findings indicate that effective V2G systems require the joint design of user contracts, charging control, renewable-energy conditions, and grid-responsive operation, rather than the maximisation of participation alone. ...
Vehicle-to-Grid (V2G) can provide flexibility to electricity systems, but its practical contribution to congestion mitigation depends not only on technical battery capacity, but also on user participation, vehicle availability, charging behaviour, and operational control. Existing research often treats participation as fixed or evaluates user acceptance separately from system performance. This study investigates which V2G system configurations are associated with lower local congestion pressure when participation emerges endogenously from heterogeneous contract preferences and behavioural feedback.
A full-year agent-based model of a stylised Dutch urban area was used to simulate the mobility, charging, contract acceptance, and V2G behaviour of 500 electric-vehicle drivers at five-minute resolution. Contract characteristics, charging policies, charging infrastructure, and renewable-energy conditions were varied across a large discrete design space. Congestion pressure was represented by a squared loss function penalising positive net-load exceedances above 600 kW. A learning-guided inverse-modelling procedure, based on a Bayesian Ridge surrogate and two independently seeded learning streams, directed simulations towards potentially favourable configurations. Each proposed configuration was evaluated through repeated stochastic simulations, and Patient Rule Induction Method was used to identify interpretable low-loss design regions.
The results show that participation is an enabling, but insufficient, condition for congestion relief. Higher participation was generally associated with lower loss, with the largest improvements occurring between low and medium-high participation levels and diminishing returns emerging at high participation. Guaranteed range was the strongest predictor of participation, followed by minimum plug-in time, while financial compensation and battery-degradation assumptions had smaller effects. Low congestion loss was most consistently associated with smart charging at residential locations, high renewable-energy coverage, and a medium-to-high guaranteed range. Smart charging and V2G performed complementary functions: controlled charging prevented avoidable EV demand from reinforcing existing peaks, while V2G reduced the remaining positive net load. Nevertheless, congestion remained concentrated in winter evenings, and a limited number of severe peaks persisted.
The inverse search identified favourable configurations relatively early and subsequently improved mainly by avoiding poor regions rather than converging towards a unique optimum. This study contributes by linking heterogeneous contract participation to realised, temporally available grid flexibility and by demonstrating how inverse modelling can identify multiple plausible socio-technical design regions. The findings indicate that effective V2G systems require the joint design of user contracts, charging control, renewable-energy conditions, and grid-responsive operation, rather than the maximisation of participation alone.
A full-year agent-based model of a stylised Dutch urban area was used to simulate the mobility, charging, contract acceptance, and V2G behaviour of 500 electric-vehicle drivers at five-minute resolution. Contract characteristics, charging policies, charging infrastructure, and renewable-energy conditions were varied across a large discrete design space. Congestion pressure was represented by a squared loss function penalising positive net-load exceedances above 600 kW. A learning-guided inverse-modelling procedure, based on a Bayesian Ridge surrogate and two independently seeded learning streams, directed simulations towards potentially favourable configurations. Each proposed configuration was evaluated through repeated stochastic simulations, and Patient Rule Induction Method was used to identify interpretable low-loss design regions.
The results show that participation is an enabling, but insufficient, condition for congestion relief. Higher participation was generally associated with lower loss, with the largest improvements occurring between low and medium-high participation levels and diminishing returns emerging at high participation. Guaranteed range was the strongest predictor of participation, followed by minimum plug-in time, while financial compensation and battery-degradation assumptions had smaller effects. Low congestion loss was most consistently associated with smart charging at residential locations, high renewable-energy coverage, and a medium-to-high guaranteed range. Smart charging and V2G performed complementary functions: controlled charging prevented avoidable EV demand from reinforcing existing peaks, while V2G reduced the remaining positive net load. Nevertheless, congestion remained concentrated in winter evenings, and a limited number of severe peaks persisted.
The inverse search identified favourable configurations relatively early and subsequently improved mainly by avoiding poor regions rather than converging towards a unique optimum. This study contributes by linking heterogeneous contract participation to realised, temporally available grid flexibility and by demonstrating how inverse modelling can identify multiple plausible socio-technical design regions. The findings indicate that effective V2G systems require the joint design of user contracts, charging control, renewable-energy conditions, and grid-responsive operation, rather than the maximisation of participation alone.