Population Dynamics for Renewables in Electricity Markets: A Minority Game View

The dominance of fluctuating and intermittent stochastic renewable energy sources (RES) has introduced uncertainty in power systems which in turn, has challenged how electricity market operate. In this context, there has been significant research in developing strategies for RESproducers, which however typically focuses on the decision process of a single producer, assuming unrealistic access to aspects of information about the power system. This paper analyzes the behavior of an entire population of stochastic producers in an electricity market using as basisa minority game: the El Farol Bar problem. We illustrate how uncomplicated strategies based on a adaptive learning rules lead to the coordination among RES producers and a Pareto efficient outcome.<br/

Mechanism design for eliciting probabilistic estimates from multiple suppliers with unknown costs and limited precision

This paper reports on the design of a novel two-stage mechanism, based on strictly proper scoring rules, that allows a centre to acquire a costly probabilistic estimate of some unknown parameter, by eliciting and fusing estimates from multiple suppliers. Each of these suppliers is capable of producing a probabilistic estimate of any precision, up to a privately known maximum, and by fusing several low precision estimates together the centre is able to obtain a single estimate with a specified minimum precision. Specifically, in the mechanism's first stage M from N agents are pre-selected by eliciting their privately known costs. In the second stage, these M agents are sequentially approached in a random order and their private maximum precision is elicited. A payment rule, based on a strictly proper scoring rule, then incentivises them to make and truthfully report an estimate of this maximum precision, which the centre fuses with others until it achieves its specified precision. We formally prove that the mechanism is incentive compatible regarding the costs, maximum precisions and estimates, and that it is individually rational. We present empirical results showing that our mechanism describes a family of possible ways to perform the pre-selection in the first stage, and formally prove that there is one that dominates all others

Mechanism design for eliciting probabilistic estimates from multiple suppliers with unknown costs and limited precision

This paper reports on the design of a novel two-stage mechanism, based on strictly proper scoring rules, that allows a centre to acquire a costly probabilistic estimate of some unknown parameter, by eliciting and fusing estimates from multiple suppliers. Each of these suppliers is capable of producing a probabilistic estimate of any precision, up to a privately known maximum, and by fusing several low precision estimates together the centre is able to obtain a single estimate with a specified minimum precision. Specifically, in the mechanism's first stage M from N agents are pre-selected by eliciting their privately known costs. In the second stage, these M agents are sequentially approached in a random order and their private maximum precision is elicited. A payment rule, based on a strictly proper scoring rule, then incentivises them to make and truthfully report an estimate of this maximum precision, which the centre fuses with others until it achieves its specified precision. We formally prove that the mechanism is incentive compatible regarding the costs, maximum precisions and estimates, and that it is individually rational. We present empirical results showing that our mechanism describes a family of possible ways to perform the pre-selection in the first stage, and formally prove that there is one that dominates all others

Price-Maker Wind Power Producer Participating in a Joint Day-Ahead and Real-Time Market

The large scale integration of stochastic renewable energy introduces significant challenges for power system operators and disputes the efficiency of the current market design.Recent research embeds the uncertain nature of renewable sources by modelling electricity markets as a two-stage stochastic problem, co-optimizing day-ahead and real-time dispatch. In this framework, we introduce a bilevel model to derive the optimal bid of a strategic wind power producer acting as price-maker both in day-ahead and real-time stages. The proposed model is a Mathematical Program with Equilibrium Constraints (MPEC) that is reformulated as a single-level Mixed-Integer LinearProgram (MILP), which can be readily solved. Our analysis shows that adopting strategic behaviour may improve producer’s expected profit as the share of wind power increases. However,this incentive diminishes in power systems where available flexible capacity is high enough to ensure an efficient market operation