Stochastic Framework for Strategic Decision-making of Load-serving Entities for Day-ahead Market

The deregulation of electricity markets has diversified the range of financial transaction modes between independent system operator (ISO), generation companies (GENCO) and load-serving entities (LSE) as the main interacting players of a day-ahead market (DAM). LSEs sell electricity to end-users and retail customers. The LSE that owns distributed generation (DG) or energy storage units can supply part of its serving loads when the nodal price of electricity rises. This opportunity stimulates them to have storage or generation facilities at the buses with higher locational marginal prices (LMP). The short-term advantage of this model is reducing the risk of financial losses for LSEs in DAMs and its long-term benefit for the LSEs and the whole system is market power mitigation by virtually increasing the price elasticity of demand. This model also enables the LSEs to manage the financial risks with a stochastic programming framework.

Particle Swarm Optimization of Electricity Market Negotiating Players Portfolio

Energy systems worldwide are complex and challenging environments. Multi-agent based simulation platforms are increasing at a high rate, as they show to be a good option to study many issues related to these systems, as well as the involved players at act in this domain. In this scope the authors’ research group has developed a multi-agent system: MASCEM (Multi- Agent System for Competitive Electricity Markets), which performs realistic simulations of the electricity markets. MASCEM is integrated with ALBidS (Adaptive Learning Strategic Bidding System) that works as a decision support system for market players. The ALBidS system allows MASCEM market negotiating players to take the best possible advantages from each market context. However, it is still necessary to adequately optimize the players’ portfolio investment. For this purpose, this paper proposes a market portfolio optimization method, based on particle swarm optimization, which provides the best investment profile for a market player, considering different market opportunities (bilateral negotiation, market sessions, and operation in different markets) and the negotiation context such as the peak and off-peak periods of the day, the type of day (business day, weekend, holiday, etc.) and most important, the renewable based distributed generation forecast. The proposed approach is tested and validated using real electricity markets data from the Iberian operator – MIBEL.

Multi-agent Simulation of Bilateral Contracting in Competitive Electricity Markets

Traditional vertically integrated power utilities around the world have evolved from monopoly structures to open markets that promote competition among suppliers and provide consumers with a choice of services. Market forces drive the price of electricity and reduce the net cost through increased competition. Electricity can be traded in both organized markets or using forward bilateral contracts. This article focuses on bilateral contracts and describes some important features of an agent-based system for bilateral trading in competitive markets. Special attention is devoted to the negotiation process, demand response in bilateral contracting, and risk management. The article also presents a case study on forward bilateral contracting: a retailer agent and a customer agent negotiate a 24h-rate tariff.

Estimating the Price of ROCs

The UK government introduced the Renewable Obligation (RO), a system of tradable quotas, to encourage the installation of renewable electricity capacity. Each unit of generation from renewables created a renewable obligation certificate (ROC). Electricity generators must either; earn ROCs through their own production, purchase ROCs in the market or pay the buy-out price to comply with the quota set by the RO. A unique aspect of this regulation is that all entities holding ROCs receive a share of the buy-out fund (the sum of all compliance purchases using the buy-out price). This set-up ensures that the difference between the market price for ROCs and the buy-out price should equal the expected share of the buy-out fund, as regulated entities arbitrage these two compliance options. The expected share of the buy-out fund depends on whether enough renewable generation is available to meet the quota. This analysis tests whether variables associated with renewable generation or electricity demand are correlated with, and thus can help predict, the price of ROCs.

Composition of electricity generation portfolios, pivotal dynamics and market prices

We use a simulation model to study how the diversification of electricity generation portfoliosinfluences wholesale prices. We find that technological diversification generally leads to lower market prices but that the relationship is mediated by the supply to demand ratio. In each demand case there is a threshold where pivotal dynamics change. Pivotal dynamics pre- and post-threshold are the cause of non-linearities in the influence of diversification on market prices. The findings are robust to our choice of behavioural parameters and match close-form solutions where those are available.

Determinants of the price of allowance in the European Union emissions trading market

Teorian mukaan täydellisen kilpailun päästöoikeuskauppamarkkinoilla päästöoikeuden hinta muodostuu markkinoilla vallitsevan päästöjen vähentämisen rajakustannuksen perusteella. Euroopan päästökauppamarkkinoilla päästöjen vähentämisen kustannuksia nostavat suhteellisen lyhyet päästökauppajaksot ja epävarmuus järjestelmän jatkuvuudesta. Toisaalta päästökaupan osallistujien yhteenlaskettu päästöjen vähentämisen tarve lienee suhteellisen vähäinen ellei olematon ensimmäisellä päästökauppajaksolla. Euroopan päästökauppamarkkinoilla päästöjen vähentämisen tarve ja päästöjenvähentämisen kustannukset ovat osittain riippuvaisia muuttuvista tekijöistä. Päästöoikeuden hintaan voivat vaikuttaa päästökauppajakson aikana tapahtuva teollisuuden suhdannevaihtelu, polttoaineiden hintojen heilahtelut sekä säätilojen vaihtelu. Päästökaupan ensimmäisinä kuukausina päästöoikeuden hintakehityksellä on ollut yhteyksiä tekijöihin, joiden muutosten tulisikin vaikuttaa päästökauppamarkkinoiden tasapainoon. Näitä tekijöitä ovat esimerkiksi polttoainemarkkinoiden ja sähkömarkkinoiden hintakehitys sekä vaihtelut säätiloissa.

A comparison of electricity spot prices simulation using ARMA-GARCH and mean-reverting models

The aim of this work is to compare two families of mathematical models for their respective capability to capture the statistical properties of real electricity spot market time series. The first model family is ARMA-GARCH models and the second model family is mean-reverting Ornstein-Uhlenbeck models. These two models have been applied to two price series of Nordic Nord Pool spot market for electricity namely to the System prices and to the DenmarkW prices. The parameters of both models were calibrated from the real time series. After carrying out simulation with optimal models from both families we conclude that neither ARMA-GARCH models, nor conventional mean-reverting Ornstein-Uhlenbeck models, even when calibrated optimally with real electricity spot market price or return series, capture the statistical characteristics of the real series. But in the case of less spiky behavior (System prices), the mean-reverting Ornstein-Uhlenbeck model could be seen to partially succeeded in this task.

A comparative value of electricity storage within GB market mechanisms

This paper investigates the value of a generic storage system within two GB market mechanisms and one ancillary service provision: the wholesale power market, the Balancing Mechanism and Firm Frequency Response (FFR). Three models are evaluated under perfect foresight and fixed horizon which is subsequently extended to explore the impact of a longer foresight on market revenues. The results show that comparatively, the balancing mechanism represents the highest source of potential revenues followed by the wholesale power market and Firm Frequency Response respectively. Longer horizons show diminishing returns, with the 1 day horizon providing the vast majority of total revenues. However storage power capacity utilization benefits from such long horizons. These results could imply that short horizons are very effective in capturing revenues in both the wholesale market and balancing mechanism whereas sizing of a storage system should take into consideration horizon foresight and accuracy for greater benefit.

Underlying dynamics of typical fluctuations of an emerging market price index: the Heston model from minutes to months

We investigate the Heston model with stochastic volatility and exponential tails as a model for the typical price fluctuations of the Brazilian São Paulo Stock Exchange Index (IBOVESPA). Raw prices are first corrected for inflation and a period spanning 15 years characterized by memoryless returns is chosen for the analysis. Model parameters are estimated by observing volatility scaling and correlation properties. We show that the Heston model with at least two time scales for the volatility mean reverting dynamics satisfactorily describes price fluctuations ranging from time scales larger than 20min to 160 days. At time scales shorter than 20 min we observe autocorrelated returns and power law tails incompatible with the Heston model. Despite major regulatory changes, hyperinflation and currency crises experienced by the Brazilian market in the period studied, the general success of the description provided may be regarded as an evidence for a general underlying dynamics of price fluctuations at intermediate mesoeconomic time scales well approximated by the Heston model. We also notice that the connection between the Heston model and Ehrenfest urn models could be exploited for bringing new insights into the microeconomic market mechanics. (c) 2005 Elsevier B.V. All rights reserved.

The Integration of Electricity from Renewable Energy Sources in the European Union Electricity Market – The case for “Smart Grids”

The power sector is to play a central role in a low carbon economy. In all the decarbonisation scenarios of the European Union renewable energy sources (RES) will be a crucial part of the solution. Current grids constitute however major bottlenecks for the future expansion of RES. Recognising the need for a modernisation of its grids, the European Union has called for the creation of a "smart supergrid" interconnecting European grids at the continental level and making them "intelligent" through the addition of information and communication technology (ICT). To implement its agenda the EU has taken a leading role in coordinating research efforts and creating a common legislative framework for the necessary modernisation of Europe’s grids. This paper intends to give both an overview and a critical appraisal of the measures taken so far by the European Union to "transform" the grids into the backbone of a decarbonised electricity system. It suggests that if competition is to play a significant role in the deployment of smart grids, the current regulatory paradigm will have to be fundamentally reassessed

Mixed models for short-run forecasting of electricity prices: Application for the Spanish market

Short-run forecasting of electricity prices has become necessary for power generation unit schedule, since it is the basis of every profit maximization strategy. In this article a new and very easy method to compute accurate forecasts for electricity prices using mixed models is proposed. The main idea is to develop an efficient tool for one-step-ahead forecasting in the future, combining several prediction methods for which forecasting performance has been checked and compared for a span of several years. Also as a novelty, the 24 hourly time series has been modelled separately, instead of the complete time series of the prices. This allows one to take advantage of the homogeneity of these 24 time series. The purpose of this paper is to select the model that leads to smaller prediction errors and to obtain the appropriate length of time to use for forecasting. These results have been obtained by means of a computational experiment. A mixed model which combines the advantages of the two new models discussed is proposed. Some numerical results for the Spanish market are shown, but this new methodology can be applied to other electricity markets as well

Reforming the Market Design of EU Electricity Markets: Addressing the Challenges of a Low-Carbon Power Sector. CEPS Task Force Report, 27 July 2015

This CEPS Task Force Report focuses on whether there is a need to adapt the EU’s electricity market design and if so, the options for doing so. In a first step, it analyses the current market trends by distinguishing between their causes and their consequences. Then, the current blueprint of EU power market design – the target model – is briefly introduced, followed by a discussion of the shortcomings of the current approach and the challenges in finding suitable solutions. The final chapter offers an inventory of solutions differentiating between recommendations shared among Task Force members and non-consensual options.