인공신경망 발전량 예측 불확실성을 고려한 가상발전소 모델예측제어

학위논문(박사) -- 서울대학교대학원 : 공과대학 기계공학부, 2022.2. 차석원.This study presents statistical and control analyses for grid resources to enhance the stability and efficiency on their operations. More specifically, this study focuses on cost-optimal model predictive control for a virtual power plant with the uncertainty in neural network power forecasting. Chapter 2 analyzes the monitoring data of solar photovoltaic power plants (PVs) distributed throughout Korea. Errors within the raw data are categorized according to their causes and symptoms. The effect of typical errors on the statistical analysis is particularly evaluated for a day-ahead hourly PV power forecast study. Chapter 3 addresses a control strategy for an energy storage system (ESS). A virtual power plant or a microgrid with a commercial building load, PV generation, and ESS charge/discharge operation is targeted as a behind-the-meter consumer-generator. Economic dispatch scheduling problem for the ESS is formulated as a mixed-integer linear program. The main goal of the control problem is optimizing the economic benefit under the time-of-use tariff and future uncertainties. Peak control as a regulation ancillary market service can be also applied during the optimization. The resulting control schedule robustly guarantees the economic benefit even under the forecast uncertainties in load power consumption and PV power generation patterns. Chapter 4 presents a more specific case of day-ahead hourly ESS scheduling. An integration of a PV and ESS is considered as a control target. Power transactions between the grid and resources are normally settled according to the time-of-use tariff. Additional incentive is provided with respect to the imbalance between the forecasted-scheduled power and actual dispatch power. This incentive policy stands for the imbalance tariff of a regulation ancillary service market. Accurate forecasting and robust scheduling functions are required for the energy management system to maximize both revenues. The PV power forecast model, which is based on a recurrent neural network, uses a convolutional neural network discriminator to decrease the gap between its open-loop one-step-ahead training and closed-loop multi-step-ahead test dynamics. This generative adversarial network concept for the model training process ensures a stable day-ahead hourly forecast performance. The robust ESS scheduling model handles the remaining forecast error as a box uncertainty set to consider the cost-optimality and cost-robustness of the control schedule. The scheduling model is formulated as a concise mixed-integer linear program to enable fast online optimization with the consideration for both transaction and incentive revenues.본 논문에서는 전력망 내 에너지자원들의 운영에 있어 안정성과 효율을 향상시키기 위한 통계분석 및 제어분석 방법과 그 결과를 서술한다. 더욱 상세하게는 인공신경망 기반 발전량 예측 결과의 불확정성을 고려한 가상발전소 전력시장 비용 최적화 모델예측제어를 목표로 한다. 제2장에서는 대한민국 전역에 분포한 태양광발전소들의 모니터링 데이터에 대한 분석 결과를 서술한다. 원시 데이터 내에 존재하는 오류들이 목록화되며, 그 원인과 증상에 따라 분류된다. 일반적으로 발생 가능한 데이터 오류들이 통계분석 결과에 미치는 영향을 확인하기 위해, 인공신경망 기반 통계적 태양광발전소 발전량 예측 모델의 성능에 대한 오류 데이터의 영향이 평가된다. 제3장에서는 전력망 내 에너지저장장치에 대한 제어 방법론을 제시한다. 상업용 건물 부하, 태양광발전소 발전, 에너지저장장치 충방전 운전을 포함하는 가상발전소 또는 마이크로그리드가 계량기 후단에 위치한 전력 소비원이자 발전원으로 제시된다. 에너지저장장치를 위한 경제적 급전계획 문제는 혼합정수 선형계획법 형태로 수식화된다. 최적화 목표는 시간대별 요금제하에서 미래 부하와 발전량 예측 불확실성을 고려한 마이크로그리드 경제적 이득 최대화이며, 피크 제어에 대한 목표 역시 보조서비스 형태로 적용 가능하다. 최적화 문제 해결을 통해 도출된 충방전 제어 스케줄은 마이크로그리드 내 부하와 발전량 예측에 대한 불확실성에도 불구하고 경제적 이득을 강건하게 보장할 수 있다. 제4장에서는 특수 조건하에서의 에너지저장장치 하루 전 시간대별 운전 스케줄 도출 방법론을 제시한다. 태양광발전소와 에너지저장장치를 물리적 또는 가상으로 연결한 집합전력자원이 고려된다. 집합전력자원과 전력망 사이의 전력 거래는 일반적인 시간대별 요금제하에서 이루어진다. 전력망 보조서비스에 해당하는 불균형 요금제가 대한민국 전력시장에서의 분산자원 중개사업자 인센티브 제도 형태로 추가 고려된다. 해당 제도 하에서 집합전력자원은 전일 예측 또는 결정된 운전 스케줄과 실제 스케줄 사이의 오차율에 따라 추가적인 인센티브를 부여받을 수 있다. 집합자원을 위한 에너지관리시스템은 시간대별 요금제와 인센티브 각각에 따른 경제적 이득을 최대화하기 위하여 정확한 예측 기능과 강건한 스케줄 도출 기능을 제공한다. 제안되는 RNN 기반 태양광발전소 발전량 예측 모델은 개방회로 형태의 학습 과정과 폐회로 형태의 사용 방식 사이의 차이를 줄이기 위해 CNN 기반 식별기를 적용한다. 모델 학습 과정에 적용되는 이 GAN 개념은 하루 전 도출한 시간대별 운전 스케줄이 안정적이도록 지원한다. 제안되는 에너지저장장치를 위한 강건 스케줄 도출 모델은 남아있는 예측 오차를 박스 형태의 불확실성 집합으로 처리하여, 도출된 제어 스케줄의 경제적 최적성과 강건성을 보장한다. 스케줄 도출 모델은 간결한 혼합정수 선형계획법 형태로 수식화되어 전력 거래 수익과 인센티브 수익 양쪽 모두를 고려한 빠른 실시간 최적화가 가능하다.1 Introduction 1 2 Analysis of Data Errors in the Solar Photovoltaic Power Plant Monitoring System Database 8 2.1 Background 9 2.2 Solar Photovoltaic Power Plants in Korea 11 2.3 Solar Photovoltaic Power Plants for Analysis 14 2.4 Errors in Static Information Data 16 2.4.1 Errors: Missing or Redundant Static Information Data 19 2.4.2 Errors: Incorrect Specification Data 20 2.5 Errors in Monitoring Data 21 2.5.1 Errors: Invalid Peak Power Values 21 2.5.2 Errors: Invalid Units 23 2.5.3 Errors: Conflictions Between Static and Monitoring Data 23 2.5.4 Errors: Garbage or Corrupted Values 24 2.5.5 Errors: Terminations of Daily Monitoring 26 2.5.6 Errors: Long-term Disconnections 27 2.5.7 Errors: Fluctuating Data Transmission Periods 28 2.5.8 Errors: Disharmonious Data Collection Timings 30 2.6 Analyses with Error Data 33 2.6.1 Effect of Incorrect Location Information 38 2.6.2 Effect of Invalid Monitoring Data Values 40 2.6.3 Effect of Missing Monitoring Data 42 2.7 Conclusion 45 2.8 Acknowledgments 47 3 Robust Scheduling of a Microgrid Energy Storage System with Ancillary Service Considerations 48 3.1 Background 49 3.2 System Architecture 52 3.3 Robust MILP Optimization 55 3.3.1 ESS Constraints 55 3.3.2 Non-Robust Approach 56 3.3.3 Intuitive Approach 58 3.3.4 ESS Power Partitioning Approach 60 3.3.5 Combined Constraint Approach 63 3.4 ESS Efficiency Maps 65 3.5 External Working Conditions 68 3.5.1 Peak Control 69 3.5.2 Demand Response 71 3.6 Simulation Results 72 3.6.1 Computation Time 72 3.6.2 Cost Robustness 76 3.6.3 Precise ESS Control 77 3.6.4 External Working Condition 79 3.7 Conclusion 81 3.8 Acknowledgments 82 4 Robust PV-BESS Scheduling for a Grid with Incentive for Forecast Accuracy 83 4.1 Background 84 4.2 PV Power Forecast Model 88 4.2.1 Data Preprocessing 88 4.2.2 RNN-based Sequence Generator 90 4.2.3 CNN-based Sequence Discriminator 93 4.2.4 Training Objectives 94 4.2.5 Training and Validation 96 4.3 Robust BESS Scheduling 98 4.3.1 Power Transaction Revenue 98 4.3.2 Forecast Accuracy Incentive 102 4.4 Results 106 4.4.1 Benchmark Models for PV Power Forecasting 106 4.4.2 Stability of the PV Power Forecast Results 107 4.4.3 Accuracy of the PV Power Forecast Results 109 4.4.4 Incentive Analysis for the PV Power Forecast Results 110 4.4.5 Effect of Input Data Accuracy on Forecast Results 111 4.4.6 Robust BESS Scheduling for the Transaction Revenue 112 4.4.7 Computation Speed of the Scheduling Problems 116 4.4.8 Online Optimization for the Incentive Revenue 117 4.5 Conclusion 119 4.6 Appendix 120 4.6.1 A Toy Example for the Robust Optimization Result 120 4.7 Acknowledgments 121 5 Conclusion 122 Bibliography 125박

Similarity-Based Chained Transfer Learning for Energy Forecasting with Big Data

Smart meter popularity has resulted in the ability to collect big energy data and has created opportunities for large-scale energy forecasting. Machine Learning (ML) techniques commonly used for forecasting, such as neural networks, involve computationally intensive training typically with data from a single building or a single aggregated load to predict future consumption for that same building or aggregated load. With hundreds of thousands of meters, it becomes impractical or even infeasible to individually train a model for each meter. Consequently, this paper proposes Similarity-Based Chained Transfer Learning (SBCTL), an approach for building neural network-based models for many meters by taking advantage of already trained models through transfer learning. The first model is trained in a traditional way whereas all other models transfer knowledge from the existing models in a chain-like manner according to similarities between energy consumption profiles. A Recurrent Neural Network (RNN) was used as the base forecasting model, two initialization techniques were considered, and different similarity measures were explored. The experiments show that SBCTL achieves accuracy comparable to traditional ML training while taking only a fraction of time

Bu y on Intraday Market or not: A Deep Learning Approach :A decision tool for buyers in the Norwegian electricity markets to decide optimal market to purchase electricity

As the share of variable renewable energy sources increases, so does the need for near-delivery offloading of surplus electricity. The availability of potentially cheap energy sources in intraday markets begs warrants the reconsideration of a potentially overlooked market. From a power buying perspective, this thesis has applied promising deep neural network techniques to produce accurate electricity price forecasts before day-ahead market closure. Architectures tested in this thesis include long short-term memory (LSTM), gated recurrent units (GRU), deep autoregressive models (DeepAR) and temporal fusion transformers (TFT). Using nested cross-validation scheme, we seek to better approximate the generalization error of our models. LSTM and GRU models are found to be the best performing, in day-ahead and intraday markets, beating the benchmark measured in MAE by 30.6 % and 29 %, respectively. The increase in performance achieved by deep neural architectures are found to be particularly prominent in periods of high price volatility. Our overall goal has been the creation of decision tool, to be used by an electricity buyer to determine optimal electricity market for a given set of delivery hours. The results presented in this thesis are based on the NO2 power region (South Norway) as a result of its relative intraday liquidity. We implement the decision tool by means of a a probabilistic classifier trained specifically on the forecasts of the optimal deep neural architectures. We find that the use of a probabilistic classifier increase classification performance when compared to using sign-difference of the forecasts directly. Despite numerous potential error sources, our decision tool is shown to increase expected marginal profits when compared to a day-ahead-only trading strategy by testing in a out-ofsample simulated “production” environment. We model a decision tool to fit the needs of various risk profiles, and find that higher risk tolerance warrants higher profits. Though beyond the scope of this thesis, the general outline of this decision tool can be modified and extended to fit the needs of power producers.nhhma

Data Analytics and Machine Learning to Enhance the Operational Visibility and Situation Awareness of Smart Grid High Penetration Photovoltaic Systems

Electric utilities have limited operational visibility and situation awareness over grid-tied distributed photovoltaic systems (PV). This will pose a risk to grid stability when the PV penetration into a given feeder exceeds 60% of its peak or minimum daytime load. Third-party service providers offer only real-time monitoring but not accurate insights into system performance and prediction of productions. PV systems also increase the attack surface of distribution networks since they are not under the direct supervision and control of the utility security analysts. Six key objectives were successfully achieved to enhance PV operational visibility and situation awareness: (1) conceptual cybersecurity frameworks for PV situation awareness at device, communications, applications, and cognitive levels; (2) a unique combinatorial approach using LASSO-Elastic Net regularizations and multilayer perceptron for PV generation forecasting; (3) applying a fixed-point primal dual log-barrier interior point method to expedite AC optimal power flow convergence; (4) adapting big data standards and capability maturity models to PV systems; (5) using K-nearest neighbors and random forests to impute missing values in PV big data; and (6) a hybrid data-model method that takes PV system deration factors and historical data to estimate generation and evaluate system performance using advanced metrics. These objectives were validated on three real-world case studies comprising grid-tied commercial PV systems. The results and conclusions show that the proposed imputation approach improved the accuracy by 91%, the estimation method performed better by 75% and 10% for two PV systems, and the use of the proposed forecasting model improved the generalization performance and reduced the likelihood of overfitting. The application of primal dual log-barrier interior point method improved the convergence of AC optimal power flow by 0.7 and 0.6 times that of the currently used deterministic models. Through the use of advanced performance metrics, it is shown how PV systems of different nameplate capacities installed at different geographical locations can be directly evaluated and compared over both instantaneous as well as extended periods of time. The results of this dissertation will be of particular use to multiple stakeholders of the PV domain including, but not limited to, the utility network and security operation centers, standards working groups, utility equipment, and service providers, data consultants, system integrator, regulators and public service commissions, government bodies, and end-consumers

Transfer learning for smart buildings: A critical review of algorithms, applications, and future perspectives

Smart buildings play a crucial role toward decarbonizing society, as globally buildings emit about one-third of greenhouse gases. In the last few years, machine learning has achieved a notable momentum that, if properly harnessed, may unleash its potential for advanced analytics and control of smart buildings, enabling the technique to scale up for supporting the decarbonization of the building sector. In this perspective, transfer learning aims to improve the performance of a target learner exploiting knowledge in related environments. The present work provides a comprehensive overview of transfer learning applications in smart buildings, classifying and analyzing 77 papers according to their applications, algorithms, and adopted metrics. The study identified four main application areas of transfer learning: (1) building load prediction, (2) occupancy detection and activity recognition, (3) building dynamics modeling, and (4) energy systems control. Furthermore, the review highlighted the role of deep learning in transfer learning applications that has been used in more than half of the analyzed studies. The paper also discusses how to integrate transfer learning in a smart building's ecosystem, identifying, for each application area, the research gaps and guidelines for future research directions

CUDA-bigPSF: An optimized version of bigPSF accelerated with Graphics Processing Unit

Accurate and fast short-term load forecasting is crucial in efficiently managing energy production and distribution. As such, many different algorithms have been proposed to address this topic, including hybrid models that combine clustering with other forecasting techniques. One of these algorithms is bigPSF, an algorithm that combines K-means clustering and a similarity search optimized for its use in distributed environments. The work presented in this paper aims to improve the time required to execute the algorithm with two main contributions. First, some of the issues of the original proposal that limited the number of cores simultaneously used are studied and highlighted. Second, a version of the algorithm optimized for Graphics Processing Unit (GPU) is proposed, solving the previously mentioned issues while taking into account the GPU architecture and memory structure. Experimentation was done with seven years of real-world electric demand data from Uruguay. Results show that the proposed algorithm executed consistently faster than the original version, achieving speedups up to 500 times faster during the training phase.Funding for open access charge: Universidad de Granada / CBUAGrant PID2020-112495RB-C21 funded by MCIN/ AEI /10.13039/501100011033I + D + i FEDER 2020 project B-TIC-42-UGR2

Air temperature forecasting using machine learning techniques: a review

Efforts to understand the influence of historical climate change, at global and regional levels, have been increasing over the past decade. In particular, the estimates of air temperatures have been considered as a key factor in climate impact studies on agricultural, ecological, environmental, and industrial sectors. Accurate temperature prediction helps to safeguard life and property, playing an important role in planning activities for the government, industry, and the public. The primary aim of this study is to review the different machine learning strategies for temperature forecasting, available in the literature, presenting their advantages and disadvantages and identifying research gaps. This survey shows that Machine Learning techniques can help to accurately predict temperatures based on a set of input features, which can include the previous values of temperature, relative humidity, solar radiation, rain and wind speed measurements, among others. The review reveals that Deep Learning strategies report smaller errors (Mean Square Error = 0.0017 °K) compared with traditional Artificial Neural Networks architectures, for 1 step-ahead at regional scale. At the global scale, Support Vector Machines are preferred based on their good compromise between simplicity and accuracy. In addition, the accuracy of the methods described in this work is found to be dependent on inputs combination, architecture, and learning algorithms. Finally, further research areas in temperature forecasting are outlined