电力市场大数据分析(英文版)

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作者：陈启鑫

出版社：科学出版社

本类榜单：工业技术

分类：工业技术 > 电工技术 > 输配电工程、电力网及电力系统

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ISBN：9787030715166
装帧：一般胶版纸
册数：暂无
重量：暂无
开本：B5
页数：304
出版时间：2022-10-01
条形码：9787030715166 ; 978-7-03-071516-6

本书特色

Undergraduatestudent，graduatestudent，powersystemengineerandparticipant

内容简介

Thisbookaimstosolvesomekeyproblemsinthedecisionandoptimizationprocedureforpowermarketorganizersandparticipantsindata-drivenapproaches.Itbeginswithanoverviewofthepowermarketdataandanalyzesontheircharacteristicsandimportanceformarketclearance.Thenitdiscussestheessentialproblemofbusloadforecastingfromtheperspectiveofmarketorganizers.Therelatedworksincludingloaduncertaintymodeling,busloadbaddatacorrection,andmonthlyloadforecasting.Thefollowingworkstrytomodelthecomplexmarketbiddingbehaviors.Specificworksincludepatternextraction,aggregatedsupplycurvesforecasting,marketsimulation,andrewardfunctionidentificationinbidding.

Contents
1　Introduction to Power Market Data　1
1.1　Overview of Electricity Markets　1
1.2　Organization and Data Disclosure of Electricity Market　4
1.2.1　Transaction Data　5
1.2.2　Price Data　7
1.2.3　Supply and Demand Data　7
1.2.4　System Operation Data　8
1.2.5　Forecast Data　8
1.2.6　Confidential Data　9
1.3　Conclusions　9
References　9
PartⅠ　Load Modeling and Forecasting
2　Load Forecasting with Smart Meter Data　13
2.1　Introduction　13
2.2　Framework　14
2.3 Ensemble Learning for Probabilistic Forecasting　16
2.3.1　Quantile Regression Averaging　17
2.3.2　Factor Quantile Regression Averaging　18
2.3.3　LASSO Quantile Regression Averaging　18
2.3.4　Quantile Gradient Boosting Regression Tree　19
2.3.5　Rolling Window-Based Forecasting　20
2.4　Case Study　20
2.4.1　Experimental Setups　2
2.4.2　Evaluation Criteria　21
2.4.3　Experimental Results　22
2.5　Conclusions　24
References　24
3　Load Data Cleaning and Forecasting　27
3.1　Introduction　27
3.2　Characteristics of Load Profiles　29
3.2.1　Low-Rank Property of Load Profiles　29
3.2.2　Bad Data in Load Profiles　30
3.3　Methodology　31
3.3.1　Framework　31
3.3.2　Singular Value Thresholding (SVT)　32
3.3.3　Quantile RF Regression　34
3.3.4　Load Forecasting　35
3.4　Evaluation Criteria　35
3.4.1　Data Cleaning-Based Criteria　35
3.4.2　Load Forecasting-Based Criteria　35
3.5　Case Study　36
3.5.1　Result of Data Cleaning　36
3.5.2　Day Ahead Point Forecast　37
3.5.3　Day Ahead Probabilistic Forecast　38
3.6　Conclusions　40
References　40
4　Monthly Electricity Consumption Forecasting　43
4.1　Introduction　43
4.2　Framework　46
4.2.1　Data Collection and Treatment　46
4.2.2　SVECM Forecasting　47
4.2.3　Self-adaptive Screening　48
4.2.4　Novelty and Characteristics of SAS-SVECM　48
4.3　Data Collection and Treatment　48
4.3.1　Data Collection and Tests　49
4.3.2　Seasonal Adjustments Based on X-12-ARIMA　49
4.4　SVECM Forecasting　49
4.4.1　VECM Forecasting　49
4.4.2　Time Series Extrapolation Forecasting　52
4.5　Self-adaptive Screening　53
4.5.1　Influential EEF Identification　53
4.5.2　Influential EEF Grouping　53
4.5.3　Forecasting Performance Evaluation Considering Different EEF Groups　55
4.6　Case Study　56
4.6.1　Basic Data and Tests　56
4.6.2　Electricity Consumption Forecasting Performance Without SAS　58
4.6.3　EC Forecasting Performance with SAS　61
4.6.4　SAS-SVECM Forecasting Comparisons with Other Forecasting Methods　65
4.7　Conclusions　67
References　67
5　Probabilistic Load Forecasting　71
5.1　Introduction　71
5.2　Data and Model　73
5.2.1　Load Dataset Exploration　73
5.2.2　Linear Regression Model Considering Recency-Effects　73
5.3　Pre-Lasso Based　Feature Selection　76
5.4　Sparse Penalized　Quantile　Regression (Quantile-Lasso)　77
5.4.1　Problem Formulation　77
5.4.2　ADMM Algorithm　78
5.5　Implementation　80
5.6　Case Study　81
5.6.1　Experiment Setups　81
5.6.2　Results　82
5.7　Concluding Remarks　86
References　86
Part Ⅱ　Electricity Price Modeling and Forecasting
6　Subspace Characteristics of LMP Data　91
6.1　Introduction　91
6.2　Model and Distribution of LMP　93
6.3　Methodology　
6.3.1　Problem Formulation　96
6.3.2　Basic Framework　97
6.3.3　Principal Component Analysis　98
6.3.4　Recursive Basis Search (Bottom-Up)　98
6.3.5　Hyperplane Detection (Top-down)　100
6.3.6　Short Summary　103
6.4　Case Study　103
6.4.1　Case 1: IEEE 30-Bus System　104
6.4.2　Case 2: IEEE 118-Bus System　106
6.4.3　Case 3: Illinois 200-Bus System　106
6.4.4　Case 4: Southwest Power Pool (SPP)　107
6.4.5　Time Consumption　108
6.5　Discussion and Conclusion　110
6.5.1　Discussion on Potential Applications　110
6.5.2　Conclusion　110
References　111
7　Day-Ahead Electricity Price Forecasting　113
7.1　Introduction　113
7.2　Problem Formulation　116
7.2.1　Decomposition of LMP　116
7.2.2　Short-Term Forecast for Each Component　117
7.2.3　Summation and Stacking of Individual Forecasts　118
7.3　Methodology　119
7.3.1　Framework　119
7.3.2　Feature Engineering　121
7.3.3　Regression Model Selection and Parameter Tuning　122
7.3.4　Model Stacking with Robust Regression　123
7.3.5　Metrics　124
7.4　Case Study　124
7.4.1　Model Selection Results　125
7.4.2　Componential Results　126
7.4.3　Stacking Results (Overall Improvements)　128
7.4.4　Error Distribution Analysis　129
7.5　Conclusion　132
References　132
8　Economic Impact of Price Forecasting Error　135
8.1　Introduction　135
8.2　General Bidding Models　137
8.2.1　Deterministic Bidding Model　138
8.2.2　Stochastic Bidding Model　139
8.3　Methodology and Framework　141
8.3.1　Forecasting Error Modeling　141
8.3.2　Multiparametric Linear Programming (MPLP)Theory　141
8.3.3　Error Impact Formulation　142
8.3.4　Overall Framework　144
8.4　Case Study　145
8.4.1　Measurement of STPF Error Level　145
8.4.2　Case　1: LSE with Deman

展开全部

节选

Chapter 1 Introduction to Power Market Data 1.1　Overview of Electricity Markets Electricity is widely used as a kind of efficient and convenient energy. The trading of electricity not only has typical characteristics of bulk commodity trading but also must meet certain constraints of physical laws. Hogan pointed out that a successful electricity market should be based on the principle of “bid-based，security-constrained economic dispatch (SCED)，，[1]. At present， the electricity spot markets in the world (including markets in the U.S.， EU， Australia， and other countries， as well as the pilot market in the trial operation stage in China) basically follow this principle. “Bid-based” refers to that market participants need to declare their own cost or utility of generating or purchasing electricity， so as to form the supply and demand curves of the market. The SCED model is usually a linear programming model whose objective function is the maximization of social welfare. The balance of energy， transmission capacity， as well as upper and lower limits of generation are included in the constraints of SCED. The optimal solution determines the cleared capacity of the market participants， and the shadow prices of the constraints are used to calculate the locational marginal prices (LMP)， representing the marginal cost of the power system caused by the incremental demand at each node. Figure 1.1 shows the timeline of wholesale electricity markets organized by regional transmission operators (RTOs) and independent system operators (ISOs) in the U.S. and Canada. The deregulation of electricity markets in the U.S. began in the 1990s. At present， there are seven ISO/RTO markets in the U.S.， including PJM [3]， CAISO [4]， NYISO [5]， etc.， most of which are under the regulation of the Federal Energy Regulatory Commission (FERC) [6]， except for the ERCOT [7] in Texas. Take PJM， the earliest deregulated market in the U.S.，as an example [8]. PJM is located in the northeast of the U.S.， and its main management area covers Pennsylvania， New Jersey， and Maryland. In 1997，PJM opened its first bid-based energy market. Later that year， the FERC approved PJM as the nation’s first fully functioning ISO. Then， from 1998 to 2001，PJM completed the implementation of LMP mechanism， financial transmission right (FTR)， day-ahead market， and ancillary services market of load frequency regulation. PJM became the nation’s first fully functioning RTO in 2002. After 2002，PJM focuses on the integration of other small regional transmission networks and expansion of the wholesale market scale. Currently， the wholesale markets in the U.S. adopt a two-settlement (i.e.， day-ahead and real-time settlement) system and use the LMP calculated based on SCED models for payment. The goal of electricity market deregulation in EU is to establish an integrated electricity market for the whole union. As EU has many member countries， its implementation of cross-border market coupling is divided into several steps. In the first step， national markets were created， starting from the partial market opening in 1999. As the next step， interconnectors were forced to open to market-based capacity allocation from 2003. Then， market coupling was established and expanded after 2005 [10]. By 2019， the EU power system had covered 36 countries and 43 transmission system operators (TSO)， and the European power exchange center EPEX SPOT [11] is responsible for cross-border transactions as well as day-ahead and intra-day coupling markets. The EU power market has completed unified day-ahead operation of 25 countries and intra-day operation of 14 countries. Compared with the U.S. electricity markets， a significant difference of the EU market is the adoption of zonal price instead of the nodal price scheme， which usually requires sufficient transmission capacity in each zone to avoid congestion and ensure a uniform zonal price. If congestion occurs， the local TSO needs to redispatch the units in the zone and make certain compensation. Figure 1.2 shows the main timeline of electricity market coupling in EU. The recent deregulation of China’s power sector started with the release of the ninth document entitled Several Opinions of the CPC Central Committee and the State Council on Further Deepening the Reform of the Electric Power System in March 2015. At present， the construction and operation of medium and long-term markets have been completed in all provinces of China. The construction of spot markets has almost been finished in eight pilot provinces and will be expanded to other provinces in the future. Currently， the spot markets have entered the trial operation stage in these pilot provinces. The map in Fig. 1.3 shows the timeline for electricity spot market evolution in China. Although there are differences in the detailed rules of these pilot spot markets， the principle in their organization， transaction and settlement is similar. The data analytic m

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