Demand response within the energy-for-water-nexus - A review. ESRI WP637, October 2019

A promising tool to achieve more flexibility within power systems is demand re-sponse (DR). End-users in many strands of industry have been subject to research up to now regarding the opportunities for implementing DR programmes. One sector that has received little attention from the literature so far, is wastewater treatment. However, case studies indicate that the potential for wastewater treatment plants to provide DR services might be significant. This review presents and categorises recent modelling approaches for industrial demand response as well as for the wastewater treatment plant operation. Furthermore, the main sources of flexibility from wastewater treatment plants are presented: a potential for variable electricity use in aeration, the time-shifting operation of pumps, the exploitation of built-in redundan-cy in the system and flexibility in the sludge processing. Although case studies con-note the potential for DR from individual WWTPs, no study acknowledges the en-dogeneity of energy prices which arises from a large-scale utilisation of DR. There-fore, an integrated energy systems approach is required to quantify system and market effects effectively

Socially governed energy hub trading enabled by blockchain-based transactions

Decentralized trading schemes involving energy prosumers have prevailed in recent years. Such schemes provide a pathway for increased energy efficiency and can be enhanced by the use of blockchain technology to address security concerns in decentralized trading. To improve transaction security and privacy protection while ensuring desirable social governance, this article proposes a novel two-stage blockchain-based operation and trading mechanism to enhance energy hubs connected with integrated energy systems (IESs). This mechanism includes multienergy aggregators (MAGs) that use a consortium blockchain and its enabled proof-of-work (PoW) to transfer and audit transaction records, with social governance principles for guiding prosumers’ decision-making in the peer-to-peer (P2P) transaction management process. The uncertain nature of renewable generation and load demand are adequately modeled in the two-stage Wasserstein-based distributionally robust optimization (DRO). The practicality of the proposed mechanism is illustrated by several case studies that jointly show its ability to handle an increased renewable generation capacity, achieve a 16.7% saving in the audit cost, and facilitate 2.4% more P2P interactions. Overall, the proposed two-stage blockchain-based trading mechanism provides a practical trading scheme and can reduce redundant trading amounts by 6.5%, leading to a further reduction of the overall operation cost. Compared to the state-of-the-art benchmark methods, our mechanism exhibits significant operation cost reduction and ensures social governance and transaction security for IES and energy hubs

An Analytical Methodology To Security Constraints Management In Power System Operation

In a deregulated electricity market, Independent System Operators (ISOs) are responsible for dispatching power to the load securely, efficiently, and economically. ISO performs Security Constrained Unit Commitment (SCUC) to guarantee sufficient generation commitment, maximized social welfare and facilitating market-driven economics. A large number of security constraints would render the model impossible to solve under time requirements. Developing a method to identify the minimum set of security constraints without overcommitting is necessary to reduce Mixed Integer Linear Programming (MILP) solution time. To overcome this challenge, we developed a powerful tool called security constraint screening. The proposed approach effectively filters out non-dominating constraints by integrating virtual transactions and capturing changes online in real-time or look-ahead markets. The security-constraint screening takes advantage of both deterministic and statistical methods, which leverages mathematical modeling and historical data. Effectiveness is verified using Midcontinent Independent System Operator (MISO) data. The research also presented a data-driven approach to forecast congestion patterns in real-time utilizing machine learning applications. Studies have been conducted using real-world data. The potential benefit is to provide the day-ahead operators with a tool for supporting decision-making regarding modeling constraints

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