08:45 AM - 09:10 AM
Robust self-scheduling for a price-maker energy storage facility in the New-York state electricity market
Recent progress in energy storage have contributed to create large-scale storage facilities and to decrease their costs. This may bring economic opportunities for storage operators, especially via energy arbitrage. However, storage operation in the market could have significant impact on electricity prices. This work aims at evaluating jointly the potential operating profit for a price-maker storage facility and its impact on the electricity prices in New-York state. Based on historical data, lower and upper bounds on the supply curve of the market are constructed. These bounds are used as an input for the robust self-scheduling problem of a price-maker storage facility.
09:10 AM - 09:35 AM
Optimal Energy Supply Shift with Battery Storages
This work is concerned with the optimal management of battery storages. Saving energy during periods of low demand in order to match periods of higher load is known as energy supply shift. Despite an increasing interest towards this technology, the implementation of battery storage is not straightforward from both a technical and an economical perspective. The high battery costs, the sensitivity of battery life expectancy to deep discharge, and high uncertainty in electricity load, supply, and prices make optimal battery operation a sophisticated mathematical problem, whose precise solution seems to become a decisive factor in the deployment of battery storage technologies. This work responds to the increasing demand for sound algorithmic solutions to optimal storage control problems arising in the dispatch optimization of power supply under uncertainty. In particular, we apply a novel approach to solve discrete-time control problems arising in this context and to show how duality-based techniques can be used to assess the quality of numerical solutions.
09:35 AM - 10:00 AM
Optimal energy trading strategy for a self-scheduled Electric Vehicle fleet operator
We address the issue of bidirectional energy transfer between a fleet of electric vehicles (EVs) and the independent system operator (ISO), from the point of view of an operator that plays the twin role of consumer (G2V transfer) and producer (V2G). In this setting, we propose two bilevel programming formulations for two market structures with and without demand elasticity. In both models, the fleet operator (leader) sets multi-blocks G2V bids and V2G offers to maximize his expected profit when resolving a self-scheduling problem meeting the EVs demand. We analyze two possible objective functions where the revenue of the FO is proportional to its offer prices or to the market clearing prices which are endogenously generated as the dual variables of the power flow constraints of the inner problem. The ISO (follower) sets the input and output quantities with the aim of maximizing social welfare and the voltage angles to meet the DC power flow constraints. Inter-temporal and inter-zonal constraints are considered. The optimal leader's prices and their corresponding follower's charging and discharging quantities for each block, hour, node are used by the FO to participate in the day-ahead electricity trading market. Based on the KKT conditions of the lower level problem, we reformulate the bi-level problem as a Mathematical Program with Equilibrium Constraints containing bilinear and highly non-convex products. A mixed integer programming reformulation of the bilevel program is solved by an off-the-shelf software. The approach is illustrated by numerical experiments based on data from the Ontario power grid.