A Mid-Term DSO Market for Capacity Limits: How to Estimate Opportunity Costs of Aggregators?

A large number of mechanisms are proposed to manage potential problems in distribution networks caused by the participation of distributed energy resources (DERs) in the wholesale markets. In this paper, we first introduce a practical and straightforward mechanism, based on capacity limits, which avoids conflicts between the transmission system operator and the distribution system operators (DSOs). Using a large number of real electric vehicle (EV) commercial charging stations we then show how an EV aggregator can forecast the opportunity cost incurred by offering a mid-term capacity limit service to the DSO. This cost is computed based on the estimated profit that the aggregator could gain in the day-ahead and real-time markets. The proposed methodology guarantees robustness against evolving EV uncertainty, both in terms of service delivery and driving requirements. It also allows the use of a variety of time-series forecasting methods without forecasting electricity prices and EV scenarios. The results of our empirical analysis show the exponential increase of opportunity cost and the considerable increase of the prediction intervals as the capacity limit decreases. The produced offering curves can be used as an indication of the underutilization cost of DERs caused by the DSO’s limitations.

[1]  S. Oren,et al.  Distribution Locational Marginal Pricing Through Quadratic Programming for Congestion Management in Distribution Networks , 2015, IEEE Transactions on Power Systems.

[2]  Johan Löfberg,et al.  YALMIP : a toolbox for modeling and optimization in MATLAB , 2004 .

[3]  Daniel Esteban Morales Bondy,et al.  A clearinghouse concept for distribution-level flexibility services , 2013, IEEE PES ISGT Europe 2013.

[4]  Munther A. Dahleh,et al.  Demand Response Using Linear Supply Function Bidding , 2015, IEEE Transactions on Smart Grid.

[5]  Hoay Beng Gooi,et al.  Cost Optimal Integration of Flexible Buildings in Congested Distribution Grids , 2018, 2018 IEEE Power & Energy Society General Meeting (PESGM).

[6]  Rens Philipsen,et al.  Auctions for congestion management in distribution grids , 2016, EEM 2016.

[7]  Manuel A. Matos,et al.  Flexibility products and markets: Literature review , 2018 .

[8]  Hongyu Wu,et al.  Transactive-Market-Based Operation of Distributed Electrical Energy Storage with Grid Constraints , 2017 .

[9]  Canbing Li,et al.  Multiobjective Model of Time-of-Use and Stepwise Power Tariff for Residential Consumers in Regulated Power Markets , 2018, IEEE Systems Journal.

[10]  S. Low,et al.  Demand Response With Capacity Constrained Supply Function Bidding , 2016 .

[11]  Qiuwei Wu,et al.  Day-ahead tariffs for the alleviation of distribution grid congestion from electric vehicles , 2012 .

[12]  Marina Gonzalez Vaya,et al.  Self Scheduling of Plug-In Electric Vehicle Aggregator to Provide Balancing Services for Wind Power , 2016, IEEE Transactions on Sustainable Energy.

[13]  Kostas Margellos,et al.  Capacity Controlled Demand Side Management: A Stochastic Pricing Analysis , 2016, IEEE Transactions on Power Systems.

[14]  S. Oren,et al.  Distribution Locational Marginal Pricing for Optimal Electric Vehicle Charging Management , 2014 .

[15]  Hoay Beng Gooi,et al.  Distributed Congestion Management of Distribution Grids under Robust Flexible Buildings Operations , 2018, 2018 IEEE Power & Energy Society General Meeting (PESGM).

[16]  R. Rockafellar,et al.  Optimization of conditional value-at risk , 2000 .

[17]  Marina Gonzalez Vaya,et al.  Optimal Bidding Strategy of a Plug-In Electric Vehicle Aggregator in Day-Ahead Electricity Markets Under Uncertainty , 2015, IEEE Transactions on Power Systems.

[18]  Andreas Sumper,et al.  Optimization problem for meeting distribution system operator requests in local flexibility markets with distributed energy resources , 2018 .

[19]  R. Rockafellar,et al.  Conditional Value-at-Risk for General Loss Distributions , 2001 .

[20]  Ruiwei Jiang,et al.  Robust Unit Commitment With Wind Power and Pumped Storage Hydro , 2012, IEEE Transactions on Power Systems.

[21]  Qiuwei Wu,et al.  Review of congestion management methods for distribution networks with high penetration of distributed energy resources , 2014, IEEE PES Innovative Smart Grid Technologies, Europe.

[22]  Zofia Lukszo,et al.  Renewable Energy Sources and Responsive Demand. Do We Need Congestion Management in the Distribution Grid? , 2014, IEEE Transactions on Power Systems.

[23]  Madeleine Gibescu,et al.  Local market framework for exploiting flexibility from the end users , 2016, 2016 13th International Conference on the European Energy Market (EEM).

[24]  Luiz Augusto N. Barroso,et al.  Time-of-Use Tariff Design Under Uncertainty in Price-Elasticities of Electricity Demand: A Stochastic Optimization Approach , 2013, IEEE Transactions on Smart Grid.

[25]  Mohammad Shahidehpour,et al.  Optimal Transactive Market Operations With Distribution System Operators , 2018, IEEE Transactions on Smart Grid.

[26]  David P. Chassin,et al.  Aggregate modeling of fast-acting demand response and control under real-time pricing , 2016 .

[27]  Mohammad Shahidehpour,et al.  Two-Stage Optimal Scheduling of Electric Vehicle Charging Based on Transactive Control , 2019, IEEE Transactions on Smart Grid.

[28]  Haoran Zhao,et al.  Uncertainty Management of Dynamic Tariff Method for Congestion Management in Distribution Networks , 2016, IEEE Transactions on Power Systems.

[29]  Luis Baringo,et al.  A stochastic robust optimization approach for the bidding strategy of an electric vehicle aggregator , 2017 .

[30]  Jacquelien M. A. Scherpen,et al.  Distributed optimal control and congestion management in the universal smart energy framework , 2016, 2016 European Control Conference (ECC).