Trading and control strategies for balancing electricity markets

Derck Koolen Frederik Ruelens Fjo De Ridder Ronnie Belmans
Zie bijlage

Trading and control strategies for balancing electricity markets

Zie bijlage

Bibliografie

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booktitle={Photovoltaic Specialists Conference (PVSC), 2011 37th IEEE},

title={Image processing methods for predicting the time of cloud shadow arrivals to photovoltaic systems},

year={2011},

month={June},

keywords={clouds;distributed power generation;image processing;photovoltaic power systems;power engineering computing;power grids;PV system;cloud detection;cloud motion;cloud shadow arrival time;electricity production;energy production;image processing method;image processing tools;microgrid;photovoltaic power;power grid;power plant;sky images;solar panel;solar power generation;Brightness;Estimation;Graphical user interfaces;Image edge detection;Power generation;Sun},

doi={10.1109/PVSC.2011.6185877},

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author={Sengupta, M. and Keller, J.},

booktitle={Photovoltaic Specialists Conference (PVSC), 2012 38th IEEE},

title={PV ramping in a distributed generation environment: A study using solar measurements},

year={2012},

month={June},

keywords={distributed power generation;photovoltaic power systems;renewable energy sources;Hawaii;Lanai;NREL;National Renewable Energy Laboratory;PV arrays;PV plant size;PV plants;PV ramping;distributed generation environment;electrical grids;high-resolution solar data;high-resolution solar measurements;orographic clouds;photovoltaic generation;power production ramps;simultaneous PV production;small standalone grid;solar radiation;Distributed power generation;Generators;Laboratories;Production;Renewable energy resources;Smoothing methods;Temperature measurement;PV variability;distributed generation;ramp rates;solar irradiance},

doi={10.1109/PVSC.2012.6317681},

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author={Vandezande, L. and Saguan, M. and Meeus, L. and Glachant, J-M and Belmans, R.},

booktitle={Energy Market, 2009. EEM 2009. 6th International Conference on the European},

title={Assessment of the implementation of cross-border balancing trade between Belgium and the Netherlands},

year={2009},

month={May},

keywords={power markets;balancing services;cross-border balancing market;cross-border balancing trade;cross-border capacity constraints;cross-border procurement;Availability;Costs;Electricity supply industry;Energy resources;Pricing;Procurement},

doi={10.1109/EEM.2009.5207126},}

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author={Van der Veen, R. A C and Abbasy, A and Hakvoort, R.A},

booktitle={Energy Market (EEM), 2011 8th International Conference on the European},

title={Analysis of the impact of cross-border balancing arrangements for Northern Europe},

year={2011},

month={May},

keywords={demand side management;multi-agent systems;power markets;supply and demand;Northern Europe;agent-based analysis;area control error netting;balance responsible parties;balancing energy trading;cross-border balancing arrangements;separate markets;Analytical models;Computational modeling;Decision making;Europe;Mathematical model;Portfolios;Pricing},

doi={10.1109/EEM.2011.5953092},}

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journal={Power Systems, IEEE Transactions on},

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year={2010},

month={Aug},

volume={25},

pages={1519-1530},

keywords={Monte Carlo methods;neural nets;power engineering computing;power markets;pricing;time series;ARMAX model;Monte Carlo method;adaptive wavelet neural network model;average mean absolute percentage error;day-ahead electricity market clearing price forecast;generalized autoregressive conditional heteroscedastic model;hybrid time-series;one-period continuously compounded return series;price series;AMAPE;ARMAX;AWNN;GARCH;Monte Carlo;day-ahead price forecast;time series method;variance of forecast errors},

doi={10.1109/TPWRS.2009.2039948},

ISSN={0885-8950},}

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author={Huixin Tian and Mu Zhang and Bo Meng},

booktitle={Computer and Information Application (ICCIA), 2010 International Conference on},

title={Prediction of day-ahead electricity price based on information fusion},

year={2010},

month={Dec},

pages={457-460},

keywords={neural nets;power markets;day-ahead electricity price;information fusion;neural network;Biological neural networks;Computational modeling;Electricity;Power markets;Prediction algorithms;Predictive models;Information fusion;electricity market;electricity price prediction;neural network},

doi={10.1109/ICCIA.2010.6141635},}

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journal={Sustainable Energy, IEEE Transactions on},

title={Forecasting Electricity Spot Prices Accounting for Wind Power Predictions},

year={2013},

month={Jan},

volume={4},

pages={210-218},

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author = "Elia",

title = "Balancing",

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A Vision For Intelligent Electrical Grids

Serving the Energy User},

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title={Day-ahead optimal charging/discharging scheduling for electric vehicles in micro-grids},

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month={Sept},

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doi={10.1049/cp.2013.1806},}

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booktitle={European Energy Market (EEM), 2012 9th International Conference on the},

title="Comparing different {EV} charging strategies in liberalized power systems",

year={2012},

month={May},

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booktitle={Power Systems Conference and Exposition, 2009. PSCE '09. IEEE/PES},

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year={2009},

month={March},

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doi={10.1109/PSCE.2009.4839973},}

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author={Kok, K. and Roossien, B. and MacDougall, P. and van Pruissen, O. and Venekamp, G. and Kamphuis, R. and Laarakkers, J. and Warmer, C.},

booktitle={Power and Energy Society General Meeting, 2012 IEEE},

title={Dynamic pricing by scalable energy management systems; Field experiences and simulation results using PowerMatcher},

year={2012},

month={July},

keywords={distributed power generation;energy management systems;energy storage;power markets;power system management;pricing;smart power grids;supply and demand;wind power plants;PowerMatcher;black-start support;congestion management;demand and supply flexibility;demand and supply response;demand response;distributed generation;dynamic pricing;electricity storage;heat pumps;heating systems;household appliances;large-scale wind power generation integration;market operations;market-ready system;mass-application circumstances;micro cogeneration;power system management;real electrical vehicles;scalable energy management systems;simulated electrical vehicles;smart electricity grid technology;technology scalability;virtual power plant operations;Electricity;Heat pumps;Power generation;Scalability;Smart grids;Substations;Vehicles;Dynamic pricing;demand response;market-based control;smart grids},

doi={10.1109/PESGM.2012.6345058},

ISSN={1944-9925},}

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author={Gonzalez Vaya, M. and Andersson, G.},

booktitle={European Energy Market (EEM), 2013 10th International Conference on the},

title={Optimal bidding strategy of a plug-in electric vehicle aggregator in day-ahead electricity markets},

year={2013},

month={May},

keywords={battery powered vehicles;costing;power markets;purchasing;tendering;charging cost minimization;day-ahead electricity markets;driver end-use constraints;flexible charging;mathematical problem with equilibrium constraints;mixed-integer linear program;optimal bidding strategy;plug-in electric vehicle aggregator;purchasing;upper-level problem;vehicle driving patterns;virtual storage resource},

doi={10.1109/EEM.2013.6607304},}

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journal={Smart Grid, IEEE Transactions on},

title={Optimized Bidding of a EV Aggregation Agent in the Electricity Market},

year={2012},

month={March},

volume={3},

pages={443-452},

keywords={electric vehicles;load forecasting;optimisation;power markets;power system economics;EV aggregation agent;bidding;electric vehicle;electrical energy selling;electricity market forecasting;optimization;Batteries;Contracts;Electricity;Electricity supply industry;Load modeling;Optimization;Aggregator;electric vehicles;electricity market;forecasting;optimization},

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month={May},

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keywords={autoregressive moving average processes;power markets;pricing;wavelet transforms;ARIMA model;day-ahead electricity price forecasting;electricity market;wavelet transform;Economic forecasting;Electricity supply industry;Filtering;Power system modeling;Predictive models;Wavelet transforms;ARIMA models;electricity market;price forecasting;wavelet transform},

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booktitle={Universities Power Engineering Conference, 2004. UPEC 2004. 39th International},

title={Price forecasting in the day-ahead electricity market},

year={2004},

month={Sept},

volume={3},

pages={1303-1307 vol. 2},

keywords={load forecasting;power markets;pricing;statistical analysis;ISO;electricity market;independent system operator;load forecasting;load historical data;mean absolute percentage error;power market;price forecasting;price historical data;statistical method;Economic forecasting;Electricity supply industry;Electricity supply industry deregulation;Fuels;Load forecasting;Power system stability;Predictive models;Statistical analysis;Time of arrival estimation;Time series analysis},}

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author = {Elia},

title = "System and market overview 2012",

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year = {2013}}

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author = "{Bosman, E.}",

title = "Markets and Costs, Market Products",

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year = {2014}}

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author={Coster, E.J. and Myrzik, J. M A and Kruimer, B. and Kling, W.L.},

journal={Proceedings of the IEEE},

title={Integration Issues of Distributed Generation in Distribution Grids},

year={2011},

month={Jan},

volume={99},

pages={28-39},

keywords={cogeneration;distributed power generation;power grids;voltage control;wind turbines;CHP plants;DG units;Dutch distribution system operators;combined heat and power plants;distributed generation;distribution grids;electric power grids;voltage control;wind turbines;Cogeneration;DIstributed control;Harmonic analysis;Load flow control;Power harmonic filters;Power system planning;Voltage control;Wind turbines;Distributed generation (DG);distribution grids;grid planning;protection;voltage control},

doi={10.1109/JPROC.2010.2052776},

ISSN={0018-9219},}

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author={Ketter, W. and Collins, J. and Reddy, P. and Weerdt, M.M.},

title={{The 2013 Power Trading Agent Competition}},

year=2013,

month=May,

institution={Erasmus Research Institute of Management},

abstract={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 networ},

keywords={TAC; autonomous agents; electronic commerce; energy; policy guidance; portfolio management; power; p}}

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author={Chuang, AS. and Schwaegerl, C.},

booktitle={Integration of Wide-Scale Renewable Resources Into the Power Delivery System},

title={Ancillary services for renewable integration},

year={2009},

month={July},

keywords={renewable energy sources;ancillary services;renewable energy integration;renewable energy-supplied power;renewable integration;renewable portfolio targets;renewable resources;Availability;Costs;Frequency control;Portfolios;Power generation;Power system reliability;Renewable energy resources;Solar power generation;Uncertainty;Wind energy generation;Ancillary services;ancillary service cost;electric service reliability;imbalance energy charge;renewable energy integration;renewable portfolio standard}}

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author={Han-I Su and El Gamal, A},

journal={Power Systems, IEEE Transactions on},

title={Modeling and Analysis of the Role of Energy Storage for Renewable Integration: Power Balancing},

year={2013},

month={Nov},

volume={28},

pages={4109-4117},

keywords={distributed power generation;energy storage;renewable energy sources;closed form expressions;energy storage;greedy policy;iid zero-mean Laplace distributed process;infinite horizon stochastic control problem;multitimescale grid operation;power balancing;renewable energy source integration;renewable generation;renewable power;residual power imbalance problem;simple analytic model;stationary stored power distribution;weakly dependent stationary process;wind power data;Energy storage;renewable integration;stochastic control},

doi={10.1109/TPWRS.2013.2266667},

ISSN={0885-8950},}

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author={Baringo, L. and Conejo, AJ.},

journal={Power Systems, IEEE Transactions on},

title={Strategic Wind Power Investment},

year={2014},

month={May},

pages={1250-1260},

keywords={integer programming;investment;linear programming;power generation economics;power markets;stochastic programming;wind power plants;DA markets;MILP model;balancing markets;day-ahead markets;mixed-integer linear programming model;production deviations;static approach;stochastic complementarity model;strategic wind power investment;wind power productions;wind power units;Electricity supply industry;Investment;Mathematical model;Production;Stochastic processes;Uncertainty;Wind power generation;Electricity market;market power;mathematical program with equilibrium constraints (MPEC);strategic producer;wind power investment},

doi={10.1109/TPWRS.2013.2292859},

ISSN={0885-8950},}

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author={Conejo, AJ. and Garcia-Bertrand, R. and Carrion, M.},

booktitle={Electric Utility Deregulation and Restructuring and Power Technologies, Third International Conference on},

title={Forward trading for an electricity producer},

year={2008},

month={April},

pages={89-93},

keywords={decision making;linear programming;power markets;stochastic programming;CVaR methodology;day-ahead pool;decision-making problem;electricity market strategy;electricity producer;forward market;forward trading;hourly pool prices;linear programming;risk;stochastic programming model;Decision making;Electricity supply industry;Forward contracts;Large-scale systems;Linear programming;Random variables;Stochastic processes;Tree data structures;Uncertainty;CVaR methodology;forward market;power producer;risk;stochastic programming},

doi={10.1109/DRPT.2008.4523384},}

@ARTICLE{64,

author={Morales, J.M. and Pineda, S. and Conejo, AJ. and Carrioon, M.},

journal={Power Systems, IEEE Transactions on},

title={Scenario Reduction for Futures Market Trading in Electricity Markets},

year={2009},

month={May},

volume={24},

pages={878-888},

keywords={decision making;power markets;stochastic processes;stochastic programming;decision-making horizon;electricity energy markets;futures market trading;scenario reduction;stochastic processes;two-stage stochastic programming models;Decision making;electricity markets;scenario reduction;stochastic programming},

doi={10.1109/TPWRS.2009.2016072},

ISSN={0885-8950},}

@ARTICLE{65,

author={Baringo, L. and Conejo, AJ.},

journal={Power Systems, IEEE Transactions on},

title={Offering Strategy Via Robust Optimization},

year={2011},

month={Aug},

volume={26},

pages={1418-1425},

keywords={integer programming;linear programming;power markets;electricity market;price confidence intervals;price predictions;price taker producer;robust mixed integer linear programming;robust optimization;Electricity supply industry;Mixed integer linear programming;Optimization;Production;Robustness;Stochastic processes;Mixed-integer linear programming;optimal offering;pool;price-taker producer;robust optimization},

doi={10.1109/TPWRS.2010.2092793},

ISSN={0885-8950},}

@ARTICLE{66,

author={Baringo, L. and Conejo, AJ.},

journal={Power Systems, IEEE Transactions on},

title={Strategic Offering for a Wind Power Producer},

year={2013},

month={Nov},

volume={28},

pages={4645-4654},

keywords={integer programming;linear programming;power generation economics;power markets;stochastic programming;wind power plants;MILP problem;MPEC;balancing market price;day-ahead market;market power;optimization software;price-maker;renewable electricity producers;stochastic mathematical program with equilibrium constraint modelling;tractable mixed-integer linear programming problem;wind power producer;Context;Mathematical model;Production;Stochastic processes;Uncertainty;Wind power generation;Wind speed;Electricity market;market power;mathematical program with equilibrium constraints (MPEC);optimal offering;strategic producer;wind power},

doi={10.1109/TPWRS.2013.2273276},

ISSN={0885-8950},}

@ARTICLE{67,

title = {Well-functioning balancing markets: A prerequisite for wind power integration},

author = {Vandezande, Leen and Meeus, Leonardo and Belmans, Ronnie and Saguan, Marcelo and Glachant, Jean-Michel J-M.},

year = {2010},

journal = {Energy Policy},

volume = {38},

pages = {3146-3154},

abstract = {This article focuses on the design of balancing markets in Europe taking into account an increasing wind power penetration. In several European countries, wind generation is so far not burdened with full balancing responsibility. However, the more wind power penetration, the less bearable for the system not to allocate balancing costs to the responsible parties. Given the variability and limited predictability of wind generation, full balancing exposure is however only feasible conditionally to well-functioning balancing markets. On that account, recommendations ensuring an optimal balancing market design are formulated and their impact on wind generation is assessed. Taking market-based or cost-reflective imbalance prices as the main objective, it is advised that: (1) the imbalance settlement should not contain penalties or power exchange prices, (2) capacity payments should be allocated to imbalanced BRPs via an additive component in the imbalance price and (3) a cap should be imposed on the amount of reserves. Efficient implementation of the proposed market design may require balancing markets being integrated across borders.},

keywords = { Electricity market design Balancing services Wind power integration},

url = {http://EconPapers.repec.org/RePEc:eee:enepol:v:38:y:2010:i:7:p:3146-3154}

}

@conference{68,

author={Van der Veen, R. A C and Hakvoort, R.A},

booktitle={Energy Market, 6th International Conference on the European},

title={Balance responsibility and imbalance settlement in Northern Europe; An evaluation},

year={2009},

month={May},

keywords={power markets;Germany;Netherlands;Nordic region;Northern Europe;balance responsibility;causal diagram;imbalance settlement;liberalized power markets;performance indicators;program time unit;Continuous production;Energy consumption;Europe;Heart;Portfolios;Power markets;Power systems;Pricing;Production systems;Technology management;balance responsibility;balancing market;balancing market design;imbalance settlement},

doi={10.1109/EEM.2009.5207168},}

@TechReport{69,

author={Jean Michel Glachant and Marcelo Saguan},

title={{An Institutional Frame to Compare Alternative Market Designs in EU Electricity Balancing}},

year=2007,

month=Jan,

institution={Massachusetts Institute of Technology, Center for Energy and Environmental Policy Research},

type={Working Papers},

abstract={The so-called “electricity wholesale market” is, in fact, a sequence of several markets. The chain is closed with a provision for “balancing,” in which energy from all wholesale markets is balanced under the authority of the Transmission Grid Manager (TSO in Europe, ISO in the United States). In selecting the market design, engineers in the European Union have traditionally preferred the technical role of balancing mechanisms as “security mechanisms.” They favour using penalties to restrict the use of balancing energy by market actors. While our paper in no way disputes the importance of grid security, nor the competency of engineers to elaborate the technical rules, we wish to attract attention to the real economic consequences of alternative balancing designs. We propose a numerical simulation in the framework of a two-stage equilibrium model. This simulation allows us to compare the economic properties of designs currently existing within the European Union and to measure their fallout. It reveals that balancing designs, which are typically presented as simple variants on technical security, are in actuality alternative institutional frameworks having at least four potential economic consequences: a distortion of the forward price; an asymmetric shift in the participants’ profits; an increase in the System Operator’s revenues; and inefficiencies.},

keywords={},

}

@conference{70,

author={Bueno, M. and Moreno, M.A and Usaola, J. and Nogales, F.J.},

booktitle={Universities Power Engineering Conference (UPEC)},

title={Strategic wind energy bidding in adjustment markets},

year={2010},

month={Aug},

pages={1-6},

keywords={optimisation;power markets;wind power plants;Spanish electricity market;price prediction;random variable;stochastic optimization process;strategic wind energy bidding;wind farm;wind producer revenue;Electricity supply industry;Forecasting;Production;Uncertainty;Wind farms;Wind forecasting;Wind power generation;Electricity markets;Forecasting;Imbalance prices;Uncertainty;Wind energy},}

@conference{71,

author={Chengjun Li and Ming Zhang},

booktitle={Natural Computation, Third International Conference on},

title={Application of GARCH Model in the Forecasting of Day-Ahead Electricity Prices},

year={2007},

month={Aug},

pages={99-103},

keywords={autoregressive processes;genetic algorithms;load forecasting;power markets;power system analysis computing;power system economics;pricing;time series;Eviews software;GARCH model;day-ahead electricity price forecasting;deregulated electric power industry;generalized autoregressive conditional heteroskedastic methodology;genetic arithmetic;time series data;Arithmetic;Economic forecasting;Electricity supply industry;Electricity supply industry deregulation;Energy consumption;Genetics;Predictive models;Software performance;Technology forecasting;Time series analysis},

doi={10.1109/ICNC.2007.252},}

@ARTICLE{72,

author={Leterme, W. and Ruelens, F. and Claessens, B. and Belmans, R.},

journal={Smart Grid, IEEE Transactions on},

title={A Flexible Stochastic Optimization Method for Wind Power Balancing With PHEVs},

year={2014},

month={May},

volume={5},

pages={1238-1245},

keywords={battery storage plants;hybrid electric vehicles;load forecasting;power engineering computing;smart power grids;system monitoring;wind power;wind power plants;BRP wind balancing case;Markov decision process formulation;PHEV;balance responsible party;dynamic programming;flexible stochastic optimization method;generic charging model;multistage stochastic problem;plug-in hybrid electric vehicles;probability distribution;smart control;smart grid;time interdependence;wind power balancing;wind power forecast errors;wind power production;Dynamic programming;Heuristic algorithms;Optimization;Real-time systems;Stochastic processes;Wind forecasting;Wind power generation;Demand side management;Markov decision process;electric vehicles;stochastic optimal control;wind balancing},

doi={10.1109/TSG.2014.2302316},

ISSN={1949-3053},}

@ARTICLE{73,

author={Darabi, Z. and Ferdowsi, M.},

journal={Sustainable Energy, IEEE Transactions on},

title={Aggregated Impact of Plug-in Hybrid Electric Vehicles on Electricity Demand Profile},

year={2011},

month={Oct},

volume={2},

pages={501-508},

keywords={air pollution;demand side management;energy storage;hybrid electric vehicles;sustainable development;PHEV charging load profiles;air pollution;distributed energy storage system;electricity demand profile;fossil fuels;greenhouse gas emissions;national household travel surveys;plug-in hybrid electric vehicles;power grid;sustainable development;Data mining;Hybrid electric vehicles;Power distribution;Power grids;Demand profile;load profile;national household travel survey (NHTS);plug-in hybrid electric vehicle (PHEV);transportation electrification;vehicle-to-grid},

doi={10.1109/TSTE.2011.2158123},

ISSN={1949-3029},}

@ARTICLE{74,

author={Shapiro, A. and Homem-de-Mello , T.},

journal={Mathematical Programming},

title={A simulation-based approach to two-stage stochastic programming with recourse},

year={1998},

volume={81},

pages={301-328},}

Universiteit of Hogeschool
Msc. in Energy Engineering
Publicatiejaar
2014
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