The push for renewable energy emphasizes the need for energy storage systems(ESSs)to mitigate the unpre-dictability and variability of these sources,yet challenges such as high investment costs,sporadic utilization,an...The push for renewable energy emphasizes the need for energy storage systems(ESSs)to mitigate the unpre-dictability and variability of these sources,yet challenges such as high investment costs,sporadic utilization,and demand mismatch hinder their broader adoption.In response,shared energy storage systems(SESSs)offer a more cohesive and efficient use of ESS,providing more accessible and cost-effective energy storage solutions to overcome these obstacles.To enhance the profitability of SESSs,this paper designs a multi-time-scale resource allocation strategy based on long-term contracts and real-time rental business models.We initially construct a life cycle cost model for SESS and introduce a method to estimate the degradation costs of multiple battery groups by cycling numbers and depth of discharge within the SESS.Subsequently,we design various long-term contracts from both capacity and energy perspectives,establishing associated models and real-time rental models.Lastly,multi-time-scale resource allocation based on the decomposition of user demand is proposed.Numerical analysis validates that the business model based on long-term contracts excels over models operating solely in the real-time market in economic viability and user satisfaction,effectively reducing battery degradation,and leveraging the aggregation effect for SESS can generate an additional increase of 10.7%in net revenue.展开更多
This paper presents a planning and real-time pricing approach for EV charging stations(CSs).The approach takes the form of a bi-level model to fully consider the interest of both the government and EV charging station...This paper presents a planning and real-time pricing approach for EV charging stations(CSs).The approach takes the form of a bi-level model to fully consider the interest of both the government and EV charging station operators in the planning process.From the perspective of maximizing social welfare,the government acts as the decision-maker of the upper level that optimizes the charging price matrix,and uses it as a transfer variable to indirectly influence the decisions of the lower level operators.Then each operator at the lower level determines their scale according to the goal of maximizing their own revenue,and feeds the scale matrix back to the upper level.A Logit model is applied to predict the drivers’preference when selecting a CS.Furthermore,an improved particle swarm optimization(PSO)with the utilization of a penalty function is introduced to solve the nonlinear nonconvex bi-level model.The paper applies the proposed Bi-level planning model to a singlecenter small/medium-sized city with three scenarios to evaluate its performance,including the equipment utilization rate,payback period,traffic attraction ability,etc.The result verifies that the model performs very well in typical CS distribution scenarios with a reasonable station payback period(average 6.5 years),and relatively high equipment utilization rate,44.32%.展开更多
基金supported by National Natural Science Foundation of China(No.U2066601).
文摘The push for renewable energy emphasizes the need for energy storage systems(ESSs)to mitigate the unpre-dictability and variability of these sources,yet challenges such as high investment costs,sporadic utilization,and demand mismatch hinder their broader adoption.In response,shared energy storage systems(SESSs)offer a more cohesive and efficient use of ESS,providing more accessible and cost-effective energy storage solutions to overcome these obstacles.To enhance the profitability of SESSs,this paper designs a multi-time-scale resource allocation strategy based on long-term contracts and real-time rental business models.We initially construct a life cycle cost model for SESS and introduce a method to estimate the degradation costs of multiple battery groups by cycling numbers and depth of discharge within the SESS.Subsequently,we design various long-term contracts from both capacity and energy perspectives,establishing associated models and real-time rental models.Lastly,multi-time-scale resource allocation based on the decomposition of user demand is proposed.Numerical analysis validates that the business model based on long-term contracts excels over models operating solely in the real-time market in economic viability and user satisfaction,effectively reducing battery degradation,and leveraging the aggregation effect for SESS can generate an additional increase of 10.7%in net revenue.
基金supported by the National Natural Science Foundation of China under Grant 51807024。
文摘This paper presents a planning and real-time pricing approach for EV charging stations(CSs).The approach takes the form of a bi-level model to fully consider the interest of both the government and EV charging station operators in the planning process.From the perspective of maximizing social welfare,the government acts as the decision-maker of the upper level that optimizes the charging price matrix,and uses it as a transfer variable to indirectly influence the decisions of the lower level operators.Then each operator at the lower level determines their scale according to the goal of maximizing their own revenue,and feeds the scale matrix back to the upper level.A Logit model is applied to predict the drivers’preference when selecting a CS.Furthermore,an improved particle swarm optimization(PSO)with the utilization of a penalty function is introduced to solve the nonlinear nonconvex bi-level model.The paper applies the proposed Bi-level planning model to a singlecenter small/medium-sized city with three scenarios to evaluate its performance,including the equipment utilization rate,payback period,traffic attraction ability,etc.The result verifies that the model performs very well in typical CS distribution scenarios with a reasonable station payback period(average 6.5 years),and relatively high equipment utilization rate,44.32%.
