Dynamic Pricing, Advanced Metering, and Demand Response in Electricity Markets

Presents an overview and analysis of the possible approaches to bringing an active demand side into electricity markets. Part of a series of research reports that examines energy issues facing California

Price-based and Incentive-based Framework of Demand Response in Portugal

Demand Response is a flexibility tool that can provide several benefits to the electric power system’s operation, namely by providing ancillary services. Although several countries have similar active consumer approaches, the truth is that these methodologies are not always clear or transparent to outsiders, and even sometimes to locals (difficult interpretation of legislation). In this way, the present paper explains Portuguese price-based and incentive-based demand response strategies, and proceed with an analysis and evaluation of the current stage of their implementation. Although the programs exist and are available, their actual use are still very limited.been supported by the European Commission H2020 MSCA-RISE -2014: Marie Skłodowska-Curie project DREAM GO Enabling Demand Response for short and real-time Efficient And Market Based Smart Grid Operation An intelligent and real time simulation approach ref 641794info:eu-repo/semantics/publishedVersio

Generating system reliability optimization

Reliability optimization can be applied in both conventional and non-conventional generating system planning. This thesis is concerned with generation adequacy optimization, with emphases on applications to wind energy penetration planning and interruptible load utilization. New models, indices and techniques for generation adequacy optimization including wind turbines and interruptible load utilization have been developed in this research work. A sequential Monte Carlo simulation technique for wind power modeling and reliability assessment of a generating system was developed in the research associated with optimum wind energy penetration planning. An auto-regressive and moving average (ARMA) time series model is used to simulate the hourly wind speeds. Two new risk-based capacity benefit indicators designated as the Load Carrying Capability Benefit Ratio (LCCBR) and the Equivalent Capacity Ratio (ECR) are introduced. These two indices are used to indicate capacity benefit and credit associated with a windenergy conversion system. A bisection technique to assess them was further developed. The problem of determining the optimum site-matching windturbine parameters was studied with the LCCBR and ECR as the optimization objective functions. Sensitivity studies were conducted to show the effect of wind energy penetration level on generation capacity benefit. A procedure for optimum penetration planning was formed, which extends the methods developed for conventional generation adequacy optimization. A basic framework and techniques to conduct interruptible load analysis using sequential Monte Carlo simulation were created in the research associated with interruptible load utilization. A new index designated as the Avoidable Additional Generating Capacity (AAGC) is introduced. Bisection search techniques were developed to effectively determine the Incremental Load Carrying Capability (ILCC) and AAGC. Case studies on suitable contractual options for interruptible load customers under given conditions are also presented in this thesis. The results show that selecting a suitable set of interruptible load contractual conditions, in which various risk conditions are well matched, will achieve enhanced interruptible load carrying capability or capacity benefits. The series of case studies described in this thesis indicate that the proposed concepts, framework, models and quantitative techniques can be applied in practical engineering situations to provide a scientific basis for generating system planning

The 2012 Power Trading Agent Competition

This is the specification for the Power Trading Agent Competition for 2012 (Power TAC 2012). Power TAC is a competitive simulation that models a “liberalized” retail electrical energy market, where competing business entities or “brokers” offer energy services to customers through tariff contracts, and must then serve those customers by trading in a wholesale market. Brokers are challenged to maximize their profits by buying and selling energy in the wholesale and retail markets, subject to fixed costs and constraints. Costs include fees for publication and withdrawal of tariffs, and distribution fees for transporting energy to their contracted customers. Costs are also incurred whenever there is an imbalance between a broker’s total contracted energy supply and demand within a given time slot. The simulation environment models a wholesale market, a regulated distribution utility, and a population of energy customers, situated in a real location on Earth during a specific period for which weather data is available. The wholesale market is a relatively simple call market, similar to many existing wholesale electric power markets, such as Nord Pool in Scandinavia or FERC markets in North America, but unlike the FERC markets we are modeling a single region, and therefore we do not model location-marginal pricing. Customer models include households and a variety of commercial and industrial entities, many of which have production capacity (such as solar panels or wind turbines) as well as electric vehicles. All have “real-time” metering to support allocation of their hourly supply and demand to their subscribed brokers, and all are approximate utility maximizers with respect to tariff selection, although the factors making up their utility functions may include aversion to change and complexity that can retard uptake of marginally better tariff offers. The distribution utility models the regulated natural monopoly that owns the regional distribution network, and is responsible for maintenance of its infrastructure and for real-time balancing of supply and demand. The balancing process is a market-based mechanism that uses economic incentives to encourage brokers to achieve balance within their portfolios of tariff subscribers and wholesale market positions, in the face of stochastic customer behaviors and weather-dependent renewable energy sources. The broker with the highest bank balance at the end of the simulation wins

The 2013 Power Trading Agent Competition

This is the specification for the Power Trading Agent Competition for 2013 (Power TAC 2013). Power TAC is a competitive simulation that models a “liberalized” retail electrical energy market, where competing business entities or “brokers” offer energy services to customers through tariff contracts, and must then serve those customers by trading in a wholesale market. Brokers are challenged to maximize their profits by buying and selling energy in the wholesale and retail markets, subject to fixed costs and constraints. Costs include fees for publication and withdrawal of tariffs, and distribution fees for transporting energy to their contracted customers. Costs are also incurred whenever there is an imbalance between a broker’s total contracted energy supply and demand within a given time slot. The simulation environment models a wholesale market, a regulated distribution utility, and a population of energy customers, situated in a real location on Earth during a specific period for which weather data is available. The wholesale market is a relatively simple call market, similar to many existing wholesale electric power markets, such as Nord Pool in Scandinavia or FERC markets in North America, but unlike the FERC markets we are modeling a single region, and therefore we do not model location-marginal pricing. Customer models include households and a variety of commercial and industrial entities, many of which have production capacity (such as solar panels or wind turbines) as well as electric vehicles. All have “real-time” metering to support allocation of their hourly supply and demand to their subscribed brokers, and all are approximate utility maximizers with respect to tariff selection, although the factors making up their utility functions may include aversion to change and complexity that can retard uptake of marginally better tariff offers. The distribution utility models the regulated natural monopoly that owns the regional distribution network, and is responsible for maintenance of its infrastructure and for real-time balancing of supply and demand. The balancing process is a market-based mechanism that uses economic incentives to encourage brokers to achieve balance within their portfolios of tariff subscribers and wholesale market positions, in the face of stochastic customer behaviors and weather-dependent renewable energy sources. The broker with the highest bank balance at the end of the simulation wins

European Policies Aiming the Penetration of Distributed Energy Resources in the Energy Market

This work has been supported by the European Commission H2020 MSCA-RISE-2014: Marie Skłodowska- Curie project DREAM-GO Enabling Demand Response for short and real- time Efficient And Market Based Smart Grid Operation -An intelligent and real-time simulation approach ref 641794.Energy policies have been widely developed in the recent past as sequence of the increasing relevance of distributed energy resources potential in power systems, namely in achieving the reduction of carbon dioxide emissions gaining independence from fossil fuels. Thus, the main factions in the world, as North America and Europe, have been focusing on the implementation of new energy policies capable of managing several types of energy sources considering their decentralized characteristics. In this way, the present work provides an introduction of how the new energy policies, concerning distributed energy resources, are working towards the increase of these resources penetration in the energy mix. Some successful case studies are presented, namely from Europe, to assess the benefits of such policies to consumers, to producers and to energy market as a whole.info:eu-repo/semantics/publishedVersio

The 2016 Power Trading Agent Competition

This is the specification for the Power Trading Agent Competition for 2016 (Power TAC 2016). Power TAC is a competitive simulation that models a “liberalized” retail electrical energy market, where competing business entities or “brokers” offer energy services to customers through tariff contracts, and must then serve those customers by trading in a wholesale market. Brokers are challenged to maximize their profits by buying and selling energy in the wholesale and retail markets, subject to fixed costs and constraints; the winner of an individual “game” is the broker with the highest bank balance at the end of a simulation run. Costs include fees for publication and withdrawal of tariffs, and distribution fees for transporting energy to their contracted customers. Costs are also incurred whenever there is an imbalance between a broker’s total contracted energy supply and demand within a given time slot. The simulation environment models a wholesale market, a regulated distribution utility, and a population of energy customers, situated in a real location on Earth during a specific period for which weather data is available. The wholesale market is a relatively simple call market, similar to many existing wholesale electric power markets, such as Nord Pool in Scandinavia or FERC markets in North America, but unlike the FERC markets we are modeling a single region, and therefore we approximate locational-marginal pricing through a simple manipulation of the wholesale supply curve. Customer models include households, electric vehicles, and a variety of commercial and industrial entities, many o