Efficiency in Deregulated Electricity Markets: Offer Cost Minimization vs. Payment Cost Minimization Auction

This study of the wholesale electricity market compares the efficiency performance of the auction mechanism currently in place in U.S. markets with the performance of a proposed mechanism. The analysis highlights the importance of considering strategic behavior when comparing different institutional systems. We find that in concentrated markets, neither auction mechanism can guarantee an efficient allocation. The advantage of the current mechanism increases with increased price competition if market demand is perfectly inelastic. However, if market demand has some responsiveness to price, the superiority of the current auction with respect to efficiency is not that obvious. We present a case where the proposed auction outperforms the current mechanism on efficiency even if all offers reflect true production costs. We also find that a market designer might face a choice problem with a tradeoff between lower electricity cost and production efficiency. Some implications for social welfare are discussed as well.

Simple-Offer vs. Complex-Offer Auctions in Deregulated Electricity Markets

In my recent experimental research of wholesale electricity auctions, I discovered that the complex structure of the offers leaves a lot of room for strategic behavior, which consequently leads to anti- competitive and inefficient outcomes in the market. A specific feature of these complex-offer auctions is that the sellers submit not only the quantities and the minimum prices at which they are willing to sell, but also the start-up fees that are designed to reimburse the fixed start-up costs of the generation plants. In this paper, using the experimental method I compare the performance of two complex-offer auctions (COAs) against the performance of a simple-offer auction (SOA), in which the sellers have to recover all their generation costs --- fixed and variable ---through a uniform market-clearing price. I find that the SOA significantly reduces consumer prices and lowers price volatility. It mitigates anti-competitive effects that are present in the COAs and achieves allocative efficiency more quickly.

A decision-support system based on particle swarm optimization for multiperiod hedging in electricity markets

This paper proposes a particle swarm optimization (PSO) approach to support electricity producers for multiperiod optimal contract allocation. The producer risk preference is stated by a utility function (U) expressing the tradeoff between the expectation and variance of the return. Variance estimation and expected return are based on a forecasted scenario interval determined by a price range forecasting model developed by the authors. A certain confidence level is associated to each forecasted scenario interval. The proposed model makes use of contracts with physical (spot and forward) and financial (options) settlement. PSO performance was evaluated by comparing it with a genetic algorithm-based approach. This model can be used by producers in deregulated electricity markets but can easily be adapted to load serving entities and retailers. Moreover, it can easily be adapted to the use of other type of contracts.

Six thinking hats for electricity markets simulation: adapting meeting tools to agent decision

In almost all industrialized countries, the energy sector has suffered a severe restructuring that originated a greater complexity in market players’ interactions. The complexity that these changes brought made way for the creation of decision support tools that facilitate the study and understanding of these markets. MASCEM – “Multiagent Simulator for Competitive Electricity Markets” arose in this context providing a framework for evaluating new rules, new behaviour, and new participants in deregulated electricity markets. MASCEM uses game theory, machine learning techniques, scenario analysis and optimisation techniques to model market agents and to provide them with decision-support. ALBidS is a multiagent system created to provide decision support to market negotiating players. Fully integrated with MASCEM it considers several different methodologies based on very distinct approaches. The Six Thinking Hats is a powerful technique used to look at decisions from different perspectives. This tool’s goal is to force the thinker to move outside his habitual thinking style. It was developed to be used mainly at meetings in order to “run better meetings, make faster decisions”. This dissertation presents a study about the applicability of the Six Thinking Hats technique in Decision Support Systems, particularly with the multiagent paradigm like the MASCEM simulator. As such this work’s proposal is of a new agent, a meta-learner based on STH technique that organizes several different ALBidS’ strategies and combines the distinct answers into a single one that, expectedly, out-performs any of them.

Six thinking hats: A novel metalearner for intelligent decision support in electricity markets

The energy sector has suffered a significant restructuring that has increased the complexity in electricity market players' interactions. The complexity that these changes brought requires the creation of decision support tools to facilitate the study and understanding of these markets. The Multiagent Simulator of Competitive Electricity Markets (MASCEM) arose in this context, providing a simulation framework for deregulated electricity markets. The Adaptive Learning strategic Bidding System (ALBidS) is a multiagent system created to provide decision support to market negotiating players. Fully integrated with MASCEM, ALBidS considers several different strategic methodologies based on highly distinct approaches. Six Thinking Hats (STH) is a powerful technique used to look at decisions from different perspectives, forcing the thinker to move outside its usual way of thinking. This paper aims to complement the ALBidS strategies by combining them and taking advantage of their different perspectives through the use of the STH group decision technique. The combination of ALBidS' strategies is performed through the application of a genetic algorithm, resulting in an evolutionary learning approach.

Prognosis of after spot volumes at the Scandinavian electricity market

The Thesis is dedicated to development of an operative tool to support decision making in after spot trading on the Nordic electricity market. The basics of the Nordic electricity market, trading mechanisms on the spot and after spot markets are presented in the Thesis. Mathematical equations that describe electricity balance condition in the power system are offered. The main driving factors that impact deviation of actual electricity balance from the scheduled one (object) in the power system have been explored and mathematically defined. The behavioral model of the object and principal trends in change of state of the object under an impact of the driving factors are determined with the help of regression analysis made in Microsoft Office Excel. The behavioral model gives an indication for the total regulation volume (Elbas trades volume, volume of regulation market, balance power) for a certain hour that serves as the base input in estimating prices on the after spot markets. Proposals for development of methodologies of forecasting the after spot electricity prices are offered.

Short-term Hydropower Planning System

The Thesis gives a decision support framework that has significant impact on the economic performance and viability of a hydropower company. The studyaddresses the short-term hydropower planning problem in the Nordic deregulated electricity market. The basics of the Nordic electricity market, trading mechanisms, hydropower system characteristics and production planning are presented in the Thesis. The related modelling theory and optimization methods are covered aswell. The Thesis provides a mixed integer linear programming model applied in asuccessive linearization method for optimal bidding and scheduling decisions inthe hydropower system operation within short-term horizon. A scenario based deterministic approach is exploited for modelling uncertainty in market price and inflow. The Thesis proposes a calibration framework to examine the physical accuracy and economic optimality of the decisions suggested by the model. A calibration example is provided with data from a real hydropower system using a commercial modelling application with the mixed integer linear programming solver CPLEX.

Cross-border transmission capacity development – Experiences from the Nordic electricity markets

The liberalisation of the wholesale electricity markets has been considered an efficient way to organise the markets. In Europe, the target is to liberalise and integrate the common European electricity markets. However, insufficient transmission capacity between the market areas hampers the integration, and therefore, new investments are required. Again, massive transmission capacity investments are not usually easy to carry through. This doctoral dissertation aims at elaborating on critical determinants required to deliver the necessary transmission capacity investments. The Nordic electricity market is used as an illustrative example. This study suggests that changes in the governance structure have affected the delivery of Nordic cross-border investments. In addition, the impacts of not fully delivered investments are studied in this doctoral dissertation. An insufficient transmission network can degrade the market uniformity and may also cause a need to split the market into smaller submarkets. This may have financial impacts on market actors when the targeted efficient sharing of resources is not met and even encourage gaming. The research methods applied in this doctoral dissertation are mainly empirical ranging from a Delphi study to case studies and numerical calculations.

Assessing the competitiveness of loss allocation methods in a deregulated electricity market

This paper critically assesses several loss allocation methods based on the type of competition each method promotes. This understanding assists in determining which method will promote more efficient network operations when implemented in deregulated electricity industries. The methods addressed in this paper include the pro rata [1], proportional sharing [2], loss formula [3], incremental [4], and a new method proposed by the authors of this paper, which is loop-based [5]. These methods are tested on a modified Nordic 32-bus network, where different case studies of different operating points are investigated. The varying results obtained for each allocation method at different operating points make it possible to distinguish methods that promote unhealthy competition from those that encourage better system operation.

Profit at Risk as a Risk Management Tool in Deregulated Energy Markets

Euroopan energiamarkkinat ovat olleet viimeisen kymmenen vuoden aikana suurten muutosten alla. Markkinoiden kehitys on ollut huomattavaa myös Iso-Britanniassa, jossa sähkö- ja kaasumarkkinat ovat olleet avoinna kilpailulle jo muutamia vuosia. Ennen markkinoiden avautumista energiyhtiöt pystyivät siirtämään kaikki riskit suoraan asiakkaan kannettaviksi. Markkinoiden avautumisen myötä lisääntynyt kilpailu on kuitenkin pakottanut energiayhtiöitä ajanmukaistamaan näkemyksiään riskeistä. Riskitekijät, joista ei aiemmin tarvinnut välittää, on nyt pystyttävä tunnistamaan ja hallitsemaan. Tämä työ keskittyy hinta- ja volyymiriskien hallintaan. Rahoitusmarkkinoilla pitkään käytettyjä riskienhallintatyökaluja on otettu käyttöön myös energiamarkkinoilla. Energiamarkkinoiden piirteet poikkeavat kuitenkin rahoitusmarkkinoista, eikä näitä työkaluja voida ottaa käyttöön muutoksitta. Silti, jopa muutosten jälkeen rahoitusmarkkinoiden riskienhallitavälineet aliarvioivat energiamarkkinoiden hinta- ja volyymiriskejä. Tässä yhteydessä työssä esitetään Profit at Risk, PaR. PaR on skenaariopohjainen riskienhallinnan työkalu, joka on kehitetty erityisesti energiamarkkinoille ja täten huomioi niiden erikoispiirteet. Työn rungon muodostavat energiamarkkinoiden käyttäytyminen, hinta- ja volyymiriskitekijät sekä pohdinta miten hinta- ja volyymiriskeiltä voidaan suojautua ja miten niitä voidaan hallita. PaR-metodologiaa verrataan perinteisiin riskienhallintamenetelmiin ja työn tavoitteena on tuoda esiin ne tekijät, joiden ansiosta PaR on sopivampi työkalu energiamarkkinoiden riskienhallintaan kuin perinteiset menetelmät. Käytännön esimerkkinä työssä toimii Fortum Energy plus’n PaR –malli. Koska PaR on kehitetty erityisesti energiamarkkinoille, se huomioi täysin markkinoiden aiheuttamat hinta- ja volyymiriskit. Käytännön esimerkki kuitenkin osoittaa, että PaR menetelmästä ei ole riskienhallinnallista hyötyä ellei työkalun käyttäjällä ole täydellistä tietämystä niin energiamarkkinoista kuin markkinoiden muutoksiin vaikuttavien tekijöiden käyttäytymisestä.

Probabilistic cost benefit analysis of generation investment in a deregulated electricity market

A new methodology is proposed for the analysis of generation capacity investment in a deregulated market environment. This methodology proposes to make the investment appraisal using a probabilistic framework. The probabilistic production simulation (PPC) algorithm is used to compute the expected energy generated, taking into account system load variations and plant forced outage rates, while the Monte Carlo approach has been applied to model the electricity price variability seen in a realistic network. The model is able to capture the price and hence the profitability uncertainties for generator companies. Seasonal variation in the electricity prices and the system demand are independently modeled. The method is validated on IEEE RTS system, augmented with realistic market and plant data, by using it to compare the financial viability of several generator investments applying either conventional or directly connected generator (powerformer) technologies. The significance of the results is assessed using several financial risk measures.

(Virtual) Agents for running electricity markets

This paper describes a multi-agent based simulation (MABS) framework to construct an artificial electric power market populated with learning agents. The artificial market, named TEMMAS (The Electricity Market Multi-Agent Simulator), explores the integration of two design constructs: (i) the specification of the environmental physical market properties and (ii) the specification of the decision-making (deliberative) and reactive agents. TEMMAS is materialized in an experimental setup involving distinct power generator companies that operate in the market and search for the trading strategies that best exploit their generating units' resources. The experimental results show a coherent market behavior that emerges from the overall simulated environment.

A congestion management and transmission price simulator for competitive electricity markets

In a liberalized electricity market, the Transmission System Operator (TSO) plays a crucial role in power system operation. Among many other tasks, TSO detects congestion situations and allocates the payments of electricity transmission. This paper presents a software tool for congestion management and transmission price determination in electricity markets. The congestion management is based on a reformulated Optimal Power Flow (OPF), whose main goal is to obtain a feasible solution for the re-dispatch minimizing the changes in the dispatch proposed by the market operator. The transmission price computation considers the physical impact caused by the market agents in the transmission network. The final tariff includes existing system costs and also costs due to the initial congestion situation and losses costs. The paper includes a case study for the IEEE 30 bus power system.

VPPs information needs for effective operation in competitive electricity markets

The large increase of renewable energy sources and Distributed Generation (DG) of electricity gives place to the Virtual Power Producer (VPP) concept. VPPs may turn electricity generation by renewable sources valuable in electricity markets. Information availability and adequate decision-support tools are crucial for achieving VPPs’ goals. This involves information concerning associated producers and market operation. This paper presents ViProd, a simulation tool that allows simulating VPPs operation, focusing mainly in the information requirements for adequate decision making.

Intelligent decision making in electricity markets: simulated annealing Q-Learning

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is a multi-agent electricity market simulator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM is integrated with ALBidS, a system that provides several dynamic strategies for agents’ behavior. This paper presents a method that aims at enhancing ALBidS competence in endowing market players with adequate strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses a reinforcement learning algorithm to learn from experience how to choose the best from a set of possible actions. These actions are defined accordingly to the most probable points of bidding success. With the purpose of accelerating the convergence process, a simulated annealing based algorithm is included.