Abstract
This paper develops a two-stage stochastic and dynamically updated multi-period mixed integer linear program (SD-MILP) for optimal coordinated bidding of an electric vehicle (EV) aggregator to maximize its profit from participating in competitive day-ahead, intra-day and real-time markets. The hourly conditional value at risk (T-CVaR) is applied to model the risk of trading in different markets. The objective of two-stage SD-MILP is modeled as a convex combination of the expected profit and the T-CVaR hourly risk measure. When day-ahead, intra-day and real-time market prices and fleet mobility are uncertain, the proposed two-stage SD-MILP model yields optimal EV charging/discharging plans for day-ahead, intra-day and real-time markets at per device level. The degradation costs of EV batteries are precisely modeled. To reflect the continuous clearing nature of the intra-day and real-time markets, rolling planning is applied, which allows re-forecasting and re-dispatching. The proposed two-stage SD-MILP is used to derive a bidding curve of an aggregator managing 1000 EVs. Furthermore, the model statistics and computation time are recorded while simulating the developed algorithm with 5000 EVs.
Highlights
Due to the growing penetration of distributed energy resources (DERs), including photovoltaic panels (PVs), electric vehicles (EVs), and thermostatically controlled loads (TCLs), power systems are benefiting an increasing control flexibility, from the supply side, and from the demand side
This paper proposes a two-stage SD-MILP to prepare optimal coordinated bids for day-ahead, intra-day, and real-time markets for a profit-maximizing and risk-averse EV aggregator
The main advantage of the developed SD-MILP model as compared to other bidding strategies is that the model includes intra-day market formulation and provides a potentially attractive market place for an EV
Summary
Due to the growing penetration of distributed energy resources (DERs), including photovoltaic panels (PVs), electric vehicles (EVs), and thermostatically controlled loads (TCLs), power systems are benefiting an increasing control flexibility, from the supply side, and from the demand side. Two-stage stochastic optimal bidding models of an EV aggregator for trading in day-ahead energy and regulation markets are presented in [11,13,14,15,17]. Development of a two-stage SD-MILP optimal coordinated bidding model for an aggregator who manages numerous storage units (stationary and EVs) and trades electric power in three-settlement markets, taking into account uncertainties in market price and fleet characteristics, as well as existing market rules. This model can be used for market exchange irrespective of (1) production or consumption technology and (2) mobile or stationary storage unit.
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