Optimal Non-Zero Price Bids for EVs in Energy and Reserves Markets Using Stochastic Optimization

Abstract

In power systems, demand and supply always have to be balanced. This is becoming more challenging due to the sustained penetration of renewable energy sources and their inherit uncertain production. Because of the increasing amount of electrical vehicles (EVs), and the high capacity and flexibility of their charging process, EVs are a good candidate for providing balancing services to electric systems. We propose a stochastic optimization method for an EV aggregator that models the uncertainty of the imbalance price, the reserve prices and the probability of acceptance and deployment of reserves. The model results in an optimal charging and discharging strategy considering dayahead purchase, imbalance trading and reserve bids. Unlike previous studies, the reserve bids consists of both a quantity and an optimal price using a novel efficient formulation for the price bids. Experimental evaluation shows that the proposed stochastic optimization method results in lower costs than determin-istic and quantity-only bid solutions.

Benchmarking Flexible Electric Loads Scheduling Algorithms

“…The simulation then proceeds step by step through every time step, providing the solution method with updated information from the data provider and receiving new decisions from the solution method as illustrated in Figure 1. For a mathematical formulation of the complete problem, see [36].…”

“…A two stage stochastic optimization model (SO2) that uses binary variables to determine whether a price bid will be accepted or not. It was originally developed by Van der Linden et al [36] and improved by making the MIP formulation more tight and compact, as presented in the work where this model is used for dealing with market participation constraints [24].…”

“…It also optimizes DA, and imbalance markets participation. The algorithm is the interpretation by Van der Linden et al [36] of the solution method introduced by Sarker et al [38].…”

Data-driven Pricing and Control for Low Carbon V2G Charging Station with Balancing Services

“…It is important to take into account a maximum and minimum quantity (Q max , Q min ) to either increase or decrease energy of EVs because the price and quantity relationship can provoke negative utilities for the CS if not managed appropriately. Thus, these bid quantities are calculated from (7), assuming there aren't any utilities obtained from buying or selling electricity to EVs, the utilities are instead obtained from balancing services offered to the grid. These maximum and minimum quantities limit the bid to provide balancing services when controlling an energy increase bid (+Qbid) in ( 8) and an energy decrease bid (−Qbid) in (9).…”

“…They considered additional penalties to customers and to the energy aggregator, in case of energy variations from the day ahead. Likewise, van der Linden et al [7] showed a bidding strategy and control over bid pricing that works when the energy price is lower than the market capacity clearing price. Chen et al [8] proposed an energy management system with V2G capability and a photovoltaic (PV) CS where EVs are classified in rigid and flexible loads to support ancillary services.…”