Mathematical Modelling of Contemporary Electricity Markets

Description

Mathematical Modelling of Contemporary Electricity Markets reviews major methodologies and tools to accurately analyze and forecast contemporary electricity markets in a ways that is ideal for practitioner and academic audiences. Approaches include optimization, neural networks, genetic algorithms, co-optimization, econometrics, E3 models and energy system models. The work examines how new challenges affect power market modeling, including discussions of stochastic renewables, price volatility, dynamic participation of demand, integration of storage and electric vehicles, interdependence with other commodity markets and the evolution of policy developments (market coupling processes, security of supply). Coverage addresses all major forms of electricity markets: day-ahead, forward, intraday, balancing, and capacity. Front Matter Copyright Contributors Preface Introduction Which is the need for the book? What are its aims and scope? What problems does the book solve? What are the features and benefits of the book? Who is the target audience What is the contribution of the book in the literature? What is the structure of the book? Does its nature (book of chapters) affect its coherence and quality? References Forecasting energy demand with econometrics Introduction Methodology Methodology application Data Long-run Short run Impulse response functions (IRFs) Conclusions References An econometric approach for Germanys short-term energy demand forecasting Introduction German energy landscape Data and methodology Variables description Methods and formulas AR (p) and MA (q) ARMA and ARMAX Forecasting AR Forecast with MA Forecast with ARMA (p,q) model Forecasting accuracy Results Measuring forecasting accuracy Comparing linear regression, simple exponential smoothing and ARMAX models Conclusions Appendix References Further reading A novel adaptive day-ahead load forecast method, incorporating non-metered distributed generation: A compariso ... Introduction Comparative review of load forecasting methodologies Load forecasting methods applied by TSOs Neural networks-Mathematical background RNNs-Transition toward LSTM Long short-term memory Short-term load forecast using LSTM network Input dataset Data preprocessing, standardization LSTM model specification Result evaluation Discussion on the correlation of non-metered generation and daily loads Conclusions References Forecasting week-ahead hourly electricity prices in Belgium with statistical and machine learning methods Introduction Statistical and machine learning methods in electricity price forecasting Overview of forecasting methods Statistical methods Seasonal naïve (sNaive) Exponential smoothing (ES) Seasonally adjusted simple exponential smoothing (DSES) Seasonally adjusted exponential smoothing (DES) Multiple Aggregation Prediction Algorithm (MAPA) Multiple linear regression (MLR) Autoregressive integrated moving average (ARIMA) Machine learning methods Multi-layer perceptron (MLP) Random forest (RF) Combinations Empirical evaluation Dataset Experimental set-up Results Conclusions References Use probabilistic forecasting to model uncertainties in electricity markets-A wind power example Introduction Data source and data conditioning Data source Data conditioning Outlier detection Data preparation Distribution and correlation analysis Forecasting methodology Multiple linear regression k-Nearest neighbors algorithm Coordinate descent algorithm Probabilistic forecasting based on KDE method Probabilistic forecasting based on KDE method Model selection Results Error measures Model configuration Evaluation results Error analysis Geographical distribution Error distribution RMSE vs. quantile score Impact of NWP quality Conclusions References Modelling interlinked commodities´ prices: The case of natural gas Introduction Literature review Data and methodology Data Methodology Structural breaks Price transmission Multivariate conditional volatility Empirical results Structural breaks Price transmission Multivariate conditional volatility Conclusions References An optimization model for the economic dispatch problem in power exchanges Introduction Mathematical model Objective function Model constraints Energy supply limits Energy consumption limits Energy exchange limits Block orders-Acceptance ratio Block orders energy generation Time-flexible block orders Demand balance Case study description Results and discussion Power system´s marginal price Electricity trading Thermal units´ operation Conclusions References Power system flexibility: A methodological analytical framework based on unit commitment and economic dispatch ... Introduction Structure Methodology Preliminary evaluation of flexibility requirements Assessment of technical capability of resources to provide flexibility UCED model Basic features of the UCED model Specific issues on the formulation of the UCED model Modelling of Reserves Modelling of storage and DR Modelling of maintenance operations and hydro operation Representation of non-explicitly modeled countries Modelling of demand Examined indicators for the detailed analysis of power system operational flexibility Methodology application Case study Results Preliminary evaluation of flexibility requirements Assessment of technical capability of resources to provide flexibility Detailed analysis of power system operational flexibility Convergence of the Monte Carlo simulations Discussion Critical points Non-availability of energy versus the non-availability of adequate level of reserve Relation between flexibility characteristics and expected market outcomes Impact of demand response Identification of areas for further improvement Representation of non-explicitly modeled countries Network modelling Mid-term/short-term hydro scheduling coordination Better representation of the Intraday Market Better representation of the Balancing Market Representation of thermal efficiency of conventional power plants Future challenges Conclusions Appendix: Further results References Retailer profit maximization with the assistance of price and load forecasting processes Introduction Methodology Problem formulation Test case Results Conclusions References Modelling cross-border interactions of EU balancing markets: A focus on scarcity pricing Introduction Scarcity pricing in US markets Scarcity pricing in EU markets Cross-border considerations Structure of the chapter The stochastic equilibrium model Real time Real-time balancing platform Generators in the Netherlands Generators in Belgium Loads Belgian network operator Market clearing Day ahead Day-ahead market clearing platform Generators in Belgium Generators in the Netherlands Loads Network operator Market clearing Application on a two-zone system Market for real-time energy in both zones Market for real-time reserve capacity in Belgium but not in the Netherlands Day-ahead energy market Day-ahead reserve market Leaning on Dutch resources Discussion Equilibrium model solutions versus actual platform outcomes Institutional considerations Conclusions and perspectives Appendix: Glossary References Electricity portfolio optimization: Cost minimization using MILP Introduction Mathematical model Objective function Case description and modelling Modelling the power-plant Modelling the spot market Improving the peak load contracts Pricing the different PLCs Modelling the demand response Pricing the DR program Summing up the different models Results and discussion Conclusions References Business opportunities in the day ahead markets by storage integration: An application to the German case Introduction Trading mathematical model Practical implementation issues Data description Results and discussion Technical analysis of the trading model with BESS Financial analysis of the trading model with BESS Econometric analysis Conclusions and policy implications References The integration of dynamic demand in electricity markets: Blockchain 3.0 as an enabler of microgrid energy ex ... Introduction Demand response, storage, electric vehicles and peer-to-peer (P2P) blockchain energy trading Peer-to-peer (P2P) energy trading Demand response, storage, electric vehicles and blockchain Implementation level Technical issues on blockchain, demand response and storage Blockchain implementations applicable to energy systems Ancillary services market (demand response-voltage support/reactive energy) Blockchain demand management considerations and power system operation Algorithmic representation of blockchain Steady state and dynamic network constraints Social dimension Conclusions and future work References Optimizing CHP operational planning for participating in day-ahead power markets: The case of a coal-fired CH ... Introduction Mathematical model Objective function Constraints Feasible operation region Thermal energy balance Operation state of the CHP plant Operation state of pulverized-coal fired boilers supplying steam to the extraction-condensing turbine Start-up trajectory of pulverized coal-fired boilers supplying steam to the extraction-condensing turbine Maximum and minimum limits of combined generation Auxiliary boilers power output and fuel consumption Auxiliary boilers power output limits Must-run constraints of auxiliary boilers Start-up and shut-down trajectories of auxiliary boilers Thermal energy storage inventory balance Thermal energy storage capacity limit Thermal energy storage charge and discharge limits Thermal energy storage operation mode Storage thermal energy losses to the surroundings Uniform water temperature in the thermal energy storage Illustrative example Computational results Conclusions References Statistical analysis of power flows based on system marginal price differentials between two power systems Introduction Methodology Validation of the statistical analysis Methodology application Conclusions References EW Flex: A decentralized flexibility marketplace fostering TSO-DSO cooperation Introduction The TSO-DSO coordination issue The Energy Web Decentralized Operating System Trust layer Utility layer Toolkit layer EW Flex EW Flex architecture Decentralized vs centralized flexibility marketplaces EW Core Application registry EW Flex platform Conclusions References Further reading Forecasting electricity supply shocks: A Bayesian panel VAR analysis Introduction Data and sample Empirical methodology Results and discussion Conclusions References Formulating and estimating an energy security index: A geopolitical review of quantitative approaches Introduction Conceptualizing energy security Estimating energy security The role of energy markets Incorporating qualitative techniques Weighting and aggregating components and dimensions Using other indexes Forecasting Conclusions References Assessing the Western Balkans power systems: A case study of Serbia Introduction Mathematical formulation Objective function Constraints Electricity demand balance Operating limits Renewable energy production Hydropower generation Energy trading Ramp limits Time limits Reserve limits Reserve requirements Case study Results Energy mix Economic performance Environmental performance Reserve mix Conclusions References Evaluation of capacity expansion scenarios for the Hellenic electric sector Introduction Methodology Results Conclusions References An ex-ante market monitoring and regulation mechanism for market concentration in electricity and natural gas ... Introduction Generic framework of the ex-ante market monitoring and regulation mechanism Defining the relative markets Supply side Demand side Comparing supply with demand side Defining combination of market concentration in aggregate and disaggregate markets Mathematical framework of the ex-ante market monitoring mechanism Supply side Estimation of Indicator 1 Estimation of Indicator 2 Estimation of Indicator 3 Estimation of Indicator 4 Demand side Application in the Hellenic electricity market Application in the electricity market Supply side Market monitoring of market concentration Regulation of market concentration Lignite disaggregate market Large hydro disaggregate market Natural gas disaggregate market Renewables disaggregate market Interconnections disaggregate market Demand side Application in the natural gas market Conclusions References Index A B C D E F G H I J K L M N O P Q R S T U V W Y