A Comparison of Two Techniques for Next- Day Electricity Price Forecasting

In the framework of competitive markets, the market’s participants need energy price forecasts in order to determine their optimal bidding strategies and maximize their benefits. Therefore, if generation companies have a good accuracy in forecasting hourly prices they can reduce the risk of over/underestimating the income obtained by selling energy. This paper presents and compares two energy price forecasting tools for day-ahead electricity market: a k Weighted Nearest Neighbours (kWNN) the weights being estimated by a genetic algorithm and a Dynamic Regression (DR). Results from realistic cases based on Spanish electricity market energy price forecasting are reported

Optimal Hour-Ahead Bidding in the Real-Time Electricity Market with Battery Storage using Approximate Dynamic Programming

There is growing interest in the use of grid-level storage to smooth variations in supply that are likely to arise with increased use of wind and solar energy. Energy arbitrage, the process of buying, storing, and selling electricity to exploit variations in electricity spot prices, is becoming an important way of paying for expensive investments into grid-level storage. Independent system operators such as the NYISO (New York Independent System Operator) require that battery storage operators place bids into an hour-ahead market (although settlements may occur in increments as small as 5 minutes, which is considered near "real-time"). The operator has to place these bids without knowing the energy level in the battery at the beginning of the hour, while simultaneously accounting for the value of leftover energy at the end of the hour. The problem is formulated as a dynamic program. We describe and employ a convergent approximate dynamic programming (ADP) algorithm that exploits monotonicity of the value function to find a revenue-generating bidding policy; using optimal benchmarks, we empirically show the computational benefits of the algorithm. Furthermore, we propose a distribution-free variant of the ADP algorithm that does not require any knowledge of the distribution of the price process (and makes no assumptions regarding a specific real-time price model). We demonstrate that a policy trained on historical real-time price data from the NYISO using this distribution-free approach is indeed effective.Comment: 28 pages, 11 figure

Agent-based simulation of electricity markets: a literature review

Liberalisation, climate policy and promotion of renewable energy are challenges to players of the electricity sector in many countries. Policy makers have to consider issues like market power, bounded rationality of players and the appearance of fluctuating energy sources in order to provide adequate legislation. Furthermore the interactions between markets and environmental policy instruments become an issue of increasing importance. A promising approach for the scientific analysis of these developments is the field of agent-based simulation. The goal of this article is to provide an overview of the current work applying this methodology to the analysis of electricity markets. --

Price Wars and Collusion in the Spanish Electricity Market

We analyze the time-series of prices in the Spanish electricity market by means of a time varying-transition-probability Markov switching model. Accounting for changes in demand and cost conditions (which re°ect changes in input costs, capacity avail- ability and hydro power), we show that the time-series of prices is characterized by two signi¯cantly di®erent price levels. Based on a Green and Porter (1984)'s type of model that introduces several institutional details, we construct trigger variables that a®ect the likelihood of starting a price war. By interpreting the signs of the triggers, we are able to infer some of the properties of the collusive strategy that ¯rms might have followed. We obtain more empirical support to Green and Porter's model than previous studies

Are agent-based simulations robust? The wholesale electricity trading case

Agent-based computational economics is becoming widely used in practice. This paper explores the consistency of some of its standard techniques. We focus in particular on prevailing wholesale electricity trading simulation methods. We include different supply and demand representations and propose the Experience-Weighted Attractions method to include several behavioural algorithms. We compare the results across assumptions and to economic theory predictions. The match is good under best-response and reinforcement learning but not under fictitious play. The simulations perform well under flat and upward-slopping supply bidding, and also for plausible demand elasticity assumptions. Learning is influenced by the number of bids per plant and the initial conditions. The overall conclusion is that agent-based simulation assumptions are far from innocuous. We link their performance to underlying features, and identify those that are better suited to model wholesale electricity markets.Agent-based computational economics, electricity, market design, experience-weighted attraction (EWA), learning, supply functions, demand aggregation, initial beliefs.

Competition in Electricity Spot Markets: Economic Theory and International Experience

auctions, electricity markets

Price Wars and Collusion in the Spanish Electricity Market

\We analyze the time-series of prices in the Spanish electricity market by means of a time varying-transition-probability Markov switching model. Accounting for changes in demand and cost conditions (which reflect changes in input costs, capacity availability and hydro power), we show that the time-series of prices is characterized by two significantly different price levels. Based on a Green and Porter (1984)'s type of model that introduces several institutional details, we construct trigger variables that affect the likelihood of starting a price war. By interpreting the signs of the triggers, we are able to infer some of the properties of the collusive strategy that firms might have followed. We obtain more empirical support to Green and Porter's model than previous studies. REVISED: January 2004Electricity Markets, Collusion, Markov Switching

Understanding Strategic Bidding in Restructured Electricity Markets: A Case Study of ERCOT

We examine the bidding behavior of firms competing on ERCOT, the hourly electricity balancing market in Texas. We characterize an equilibrium model of bidding into this uniform-price divisible-good auction market. Using detailed firm-level data on bids and marginal costs of generation, we find that firms with large stakes in the market performed close to theoretical benchmarks of static, profit-maximizing bidding derived from our model. However, several smaller firms utilized excessively steep bid schedules that deviated significantly from our theoretical benchmarks, in a manner that could not be empirically accounted for by the presence of technological adjustment costs, transmission constraints, or collusive behavior. Our results suggest that payoff scale matters in firms' willingness and ability to participate in complex, strategic market environments. Finally, although smaller firms moved closer to theoretical bidding benchmarks over time, their bidding patterns contributed to productive inefficiency in this newly restructured market, along with efficiency losses due to the close-to optimal exercise of market power by larger firms.