Abstract-In this paper, a new scenario-based stochastic optimization framework is proposed for price-maker economic bidding in day-ahead and real-time markets. The presented methodology is general and can be applied to both demand and supply bids. That is, no restrictive assumptions are made on the characteristics of the pool and its agents. However, our focus is on the operation of time-shiftable loads with deadlines, because they play a central role in creating load flexibility and enhancing demand response and peak-load shaving programs. Both basic and complex time-shiftable load types are addressed, where the latter includes time-shiftable loads that are uninterruptible, have per-time-slot consumption limits or ramp constraints, or comprise several smaller time-shiftable subloads. Four innovative analytical steps are presented in order to transform the originally nonlinear and hard-to-solve price-maker economic bidding optimization problem into a tractable mixed-integer linear program. Accordingly, the global optimal solutions are found for the price and energy bids within a relatively short amount of computational time. A detailed illustrative case study along with multiple case studies based on the California energy market data are presented. It is observed that the proposed optimal price-maker economic bidding approach outperforms optimal price-maker self-scheduling as well as even-load-distribution.Index Terms-Day-ahead market, demand response, energy bids, price-maker economic bidding, price bids, real-time market, stochastic mixed-integer linear programming, time-shiftable loads. NOMENCLATURENumber of daily market intervals. Index of time.Index of random scenario. Index of sub-loads.Indexes of steps in price quota curves. Energy bid to real-time market.Price bid submitted to the day-ahead market.Cleared price in day-ahead market.Cleared price in real-time market.Dispatched energy for economic bid.Dispatched energy for self-schedule bid.th Maximum day-ahead cleared energy at a price.Minimum price at each step of function thWidth of each step in function th.Minimum energy at each step in function .Width of each step in function .Minimum energy at each step in function .Width of each step in function .On and off status of a time-shiftable load.Total cleared energy in two-settlement markets.Minimum consumption level.Maximum consumption level.Maximum ramp-down rate.Maximum ramp-up rate.Binary auxiliary variables. Continuous auxiliary variables.Energy procurement cost at day-ahead market.

In this paper we seek to optimally operate a retailer that, on one side, aggregates a group of price-responsive loads and on the other, submits block-wise demand bids to the day-ahead and real-time markets. Such a retailer/aggregator needs to tackle uncertainty both in customer behavior and wholesale electricity markets. The goal in our design is to maximize the profit for the retailer/aggregator. We derive closed-form solutions for the risk-neutral case and also provide a stochastic optimization framework to efficiently analyze the risk-averse case. In the latter, the price-responsiveness of the load is modeled by means of a nonparametric analysis of experimental random scenarios, allowing for the response model to be non-linear. The price-responsive load models are derived based on the Olympic Peninsula experiment load elasticity data. We benchmark the proposed method using data from the California ISO wholesale electricity market.

No abstract

Abstract-One year of demand bids in the California energy market are analyzed and observations are reported on type, size, shape, and other characteristics of the bids. The implications of these observations, the underlying causes, and the potentials to improve demand bids by exploiting load flexibility are discussed.

No abstract