Situation Awareness for Smart Distribution Systems

In recent years, the global climate has become variable due to intensification of the greenhouse effect, and natural disasters are frequently occurring, which poses challenges to the situation awareness of intelligent distribution networks. Aside from the continuous grid connection of distributed generation, energy storage and new energy generation not only reduces the power supply pressure of distribution network to a certain extent but also brings new consumption pressure and load impact. Situation awareness is a technology based on the overall dynamic insight of environment and covering perception, understanding, and prediction. Such means have been widely used in security, intelligence, justice, intelligent transportation, and other fields and gradually become the research direction of digitization and informatization in the future. We hope this Special Issue represents a useful contribution. We present 10 interesting papers that cover a wide range of topics all focused on problems and solutions related to situation awareness for smart distribution systems. We sincerely hope the papers included in this Special Issue will inspire more researchers to further develop situation awareness for smart distribution systems. We strongly believe that there is a need for more work to be carried out, and we hope this issue provides a useful open-access platform for the dissemination of new ideas

Data Mining in Smart Grids

Effective smart grid operation requires rapid decisions in a data-rich, but information-limited, environment. In this context, grid sensor data-streaming cannot provide the system operators with the necessary information to act on in the time frames necessary to minimize the impact of the disturbances. Even if there are fast models that can convert the data into information, the smart grid operator must deal with the challenge of not having a full understanding of the context of the information, and, therefore, the information content cannot be used with any high degree of confidence. To address this issue, data mining has been recognized as the most promising enabling technology for improving decision-making processes, providing the right information at the right moment to the right decision-maker. This Special Issue is focused on emerging methodologies for data mining in smart grids. In this area, it addresses many relevant topics, ranging from methods for uncertainty management, to advanced dispatching. This Special Issue not only focuses on methodological breakthroughs and roadmaps in implementing the methodology, but also presents the much-needed sharing of the best practices. Topics include, but are not limited to, the following: Fuzziness in smart grids computing Emerging techniques for renewable energy forecasting Robust and proactive solution of optimal smart grids operation Fuzzy-based smart grids monitoring and control frameworks Granular computing for uncertainty management in smart grids Self-organizing and decentralized paradigms for information processin

Stochastic Model Predictive Control and Machine Learning for the Participation of Virtual Power Plants in Simultaneous Energy Markets

The emergence of distributed energy resources in the electricity system involves new scenarios in which domestic consumers (end-users) can be aggregated to participate in energy markets, acting as prosumers. Every prosumer is considered to work as an individual energy node, which has its own renewable generation source, its controllable and non-controllable energy loads, or even its own individual tariffs to trade. The nodes can build aggregations which are managed by a system operator. The participation in energy markets is not trivial for individual prosumers due to different aspects such as the technical requirements which must be satisfied, or the need to trade with a minimum volume of energy. These requirements can be solved by the definition of aggregated participations. In this context, the aggregators handle the difficult task of coordinating and stabilizing the prosumers' operations, not only at an individual level, but also at a system level, so that the set of energy nodes behaves as a single entity with respect to the market. The system operators can act as a trading-distributing company, or only as a trading one. For this reason, the optimization model must consider not only aggregated tariffs, but also individual tariffs to allow individual billing for each energy node. The energy node must have the required technical and legal competences, as well as the necessary equipment to manage their participation in energy markets or to delegate it to the system operator. This aggregation, according to business rules and not only to physical locations, is known as virtual power plant. The optimization of the aggregated participation in the different energy markets requires the introduction of the concept of dynamic storage virtualization. Therefore, every energy node in the system under study will have a battery installed to store excess energy. This dynamic virtualization defines logical partitions in the storage system to allow its use for different purposes. As an example, two different partitions can be defined: one for the aggregated participation in the day-ahead market, and the other one for the demand-response program. There are several criteria which must be considered when defining the participation strategy. A risky strategy will report more benefits in terms of trading; however, this strategy will also be more likely to get penalties for not meeting the contract due to uncertainties or operation errors. On the other hand, a conservative strategy would result worse economically in terms of trading, but it will reduce these potential penalties. The inclusion of dynamic intent profiles allows to set risky bids when there exist a potential low error of forecast in terms of generation, load or failures; and conservative bids otherwise. The system operator is the agent who decides how much energy will be reserved to trade, how much to energy node self consumption, how much to demand-response program participation etc. The large number of variables and states makes this problem too complex to be solved by classical methods, especially considering the fact that slight differences in wrong decisions would imply important economic issues in the short term. The concept of dynamic storage virtualization has been studied and implemented to allow the simultaneous participation in multiple energy markets. The simultaneous participations can be optimized considering the objective of potential profits, potential risks or even a combination of both considering more advanced criteria related to the system operator's know-how. Day-ahead bidding algorithms, demand-response program participation optimization and a penalty-reduction operation control algorithm have been developed. A stochastic layer has been defined and implemented to improve the robustness inherent to forecast-dependent systems. This layer has been developed with chance-constraints, which includes the possibility of combining an intelligent agent based on a encoder-decoder arquitecture built with neural networks composed of gated recurrent units. The formulation and the implementation allow a total decouplement among all the algorithms without any dependency among them. Nevertheless, they are completely engaged because the individual execution of each one considers both the current scenario and the selected strategy. This makes possible a wider and better context definition and a more real and accurate situation awareness. In addition to the relevant simulation runs, the platform has also been tested on a real system composed of 40 energy nodes during one year in the German island of Borkum. This experience allowed the extraction of very satisfactory conclusions about the deployment of the platform in real environments.La irrupción de los sistemas de generación distribuidos en los sistemas eléctricos dan lugar a nuevos escenarios donde los consumidores domésticos (usuarios finales) pueden participar en los mercados de energía actuando como prosumidores. Cada prosumidor es considerado como un nodo de energía con su propia fuente de generación de energía renovable, sus cargas controlables y no controlables e incluso sus propias tarifas. Los nodos pueden formar agregaciones que serán gestionadas por un agente denominado operador del sistema. La participación en los mercados energéticos no es trivial, bien sea por requerimientos técnicos de instalación o debido a la necesidad de cubrir un volumen mínimo de energía por transacción, que cada nodo debe cumplir individualmente. Estas limitaciones hacen casi imposible la participación individual, pero pueden ser salvadas mediante participaciones agregadas. El agregador llevará a cabo la ardua tarea de coordinar y estabilizar las operaciones de los nodos de energía, tanto individualmente como a nivel de sistema, para que todo el conjunto se comporte como una unidad con respecto al mercado. Las entidades que gestionan el sistema pueden ser meras comercializadoras, o distribuidoras y comercializadoras simultáneamente. Por este motivo, el modelo de optimización sobre el que basarán sus decisiones deberá considerar, además de las tarifas agregadas, otras individuales para permitir facturaciones independientes. Los nodos deberán tener autonomía legal y técnica, así como el equipamiento necesario y suficiente para poder gestionar, o delegar en el operador del sistema, su participación en los mercados de energía. Esta agregación atendiendo a reglas de negocio y no solamente a restricciones de localización física es lo que se conoce como Virtual Power Plant. La optimización de la participación agregada en los mercados, desde el punto de vista técnico y económico, requiere de la introducción del concepto de virtualización dinámica del almacenamiento, para lo que será indispensable que los nodos pertenecientes al sistema bajo estudio consten de una batería para almacenar la energía sobrante. Esta virtualización dinámica definirá particiones lógicas en el sistema de almacenamiento para dedicar diferentes porcentajes de la energía almacenada para propósitos distintos. Como ejemplo, se podría hacer una virtualización en dos particiones lógicas diferentes: una de demand-response. Así, el sistema podría operar y satisfacer ambos mercados de manera simultánea con el mismo grid y el mismo almacenamiento. El potencial de estas particiones lógicas es que se pueden definir de manera dinámica, dependiendo del contexto de ejecución y del estado, tanto de la red, como de cada uno de los nodos a nivel individual. Para establecer una estrategia de participación se pueden considerar apuestas arriesgadas que reportarán más beneficios en términos de compra-venta, pero también posibles penalizaciones por no poder cumplir con el contrato. Por el contrario, una estrategia conservadora podría resultar menos beneficiosa económicamente en dichos términos de compra-venta, pero reducirá las penalizaciones. La inclusión del concepto de perfiles de intención dinámicos permitirá hacer pujas que sean arriesgadas, cuando existan errores de predicción potencialmente pequeños en términos de generación, consumo o fallos; y pujas más conservadoras en caso contrario. El operador del sistema es el agente que definirá cuánta energía utiliza para comercializar, cuánta para asegurar autoconsumo, cuánta desea tener disponible para participar en el programa de demand-response etc. El gran número de variables y de situaciones posibles hacen que este problema sea muy costoso y complejo de resolver mediante métodos clásicos, sobre todo teniendo en cuenta que pequeñas variaciones en la toma de decisiones pueden tener grandes implicaciones económicas incluso a corto plazo. En esta tesis se ha investigado en el concepto de virtualización dinámica del almacenamiento para permitir una participación simultánea en múltiples mercados. La estrategia de optimización definida permite participaciones simultáneas en diferentes mercados que pueden ser controladas con el objetivo de optimizar el beneficio potencial, el riesgo potencial, o incluso una combinación mixta de ambas en base a otros criterios más avanzados marcados por el know-how del operador del sistema. Se han desarrollado algoritmos de optimización para el mercado del day-ahead, para la participación en el programa de demand-response y un algoritmo de control para reducir las penalizaciones durante la operación mediante modelos de control predictivo. Se ha realizado la definición e implementación de un componente estocástico para hacer el sistema más robusto frente a la incertidumbre inherente a estos sistemas en los que hay tanto peso de una componente de tipo forecasing. La formulación de esta capa se ha realizado mediante chance-constraints, que incluye la posibilidad de combinar diferentes componentes para mejorar la precisión de la optimización. Para el caso de uso presentado se ha elegido la combinación de métodos estadísticos por probabilidad junto a un agente inteligente basado en una arquitectura de codificador-decodificador construida con redes neuronales compuestas de Gated Recurrent Units. La formulación y la implementación utilizada permiten que, aunque todos los algoritmos estén completamente desacoplados y no presenten dependencias entre ellos, todos se actual como la estrategia seleccionada. Esto permite la definición de un contexto mucho más amplio en la ejecución de las optimizaciones y una toma de decisiones más consciente, real y ajustada a la situación que condiciona al proceso. Además de las pertinentes pruebas de simulación, parte de la herramienta ha sido probada en un sistema real compuesto por 40 nodos domésticos, convenientemente equipados, durante un año en una infraestructura implantada en la isla alemana de Borkum. Esta experiencia ha permitido extraer conclusiones muy interesantes sobre la implantación de la plataforma en entornos reales

Modeling and Communicating Flexibility in Smart Grids Using Artificial Neural Networks as Surrogate Models

Increasing shares of renewable energies and the transition towards electric vehicles pose major challenges to the energy system. In order to tackle these in an economically sensible way, the flexibility of distributed energy resources (DERs), such as battery energy storage systems, combined heat and power plants, and heat pumps, needs to be exploited. Modeling and communicating this flexibility is a fundamental step when trying to achieve control over DERs. The literature proposes and makes use of many different approaches, not only for the exploitation itself, but also in terms of models. In the first step, this thesis presents an extensive literature review and a general framework for classifying exploitation approaches and the communicated models. Often, the employed models only apply to specific types of DERs, or the models are so abstract that they neglect constraints and only roughly outline the true flexibility. Surrogate models, which are learned from data, can pose as generic DER models and may potentially be trained in a fully automated process. In this thesis, the idea of encoding the flexibility of DERs into ANNs is systematically investigated. Based on the presented framework, a set of ANN-based surrogate modeling approaches is derived and outlined, of which some are only applicable for specific use cases. In order to establish a baseline for the approximation quality, one of the most versatile identified approaches is evaluated in order to assess how well a set of reference models is approximated. If this versatile model is able to capture the flexibility well, a more specific model can be expected to do so even better. The results show that simple DERs are very closely approximated, and for more complex DERs and combinations of multiple DERs, a high approximation quality can be achieved by introducing buffers. Additionally, the investigated approach has been tested in scheduling tasks for multiple different DERs, showing that it is indeed possible to use ANN-based surrogates for the flexibility of DERs to derive load schedules. Finally, the computational complexity of utilizing the different approaches for controlling DERs is compared

Advanced Operation and Maintenance in Solar Plants, Wind Farms and Microgrids

This reprint presents advances in operation and maintenance in solar plants, wind farms and microgrids. This compendium of scientific articles will help clarify the current advances in this subject, so it is expected that it will please the reader

Modeling and Control for Packetized Energy Management

The overarching goal in power systems operations is to deliver energy in an efficient, reliable, and economical manner. To achieve this objective, the traditional power system operating paradigm is for generation to follow variable demand. As electrification and decarbonization policies are pursued, the levels of variable, renewable generation will increase, which will require that power system operator think beyond supply follows demand. This means that one needs to consider the potential flexibility provided by, for instance, internet-enabled, connected, and responsive loads, which are part of the broad class of behind-the-meter distributed energy resources (DERs). The research work presented in this dissertation is concerned with coordinating large populations of distributed energy resources (DERs) for providing services to the electric grid. DERs are flexible in the sense that their power consumption can be deferred in time, because DERs store energy in some form while serving the end-use customer. For example, electric water heaters store thermal energy in the form of hot-water in the tank. Therefore, aggregate fleets of DERs are an inexpensive source of virtual energy storage that the utilities can tap into for the purpose of balancing the variability in distributed renewable generation such as solar PV, wind etc. In this work, a novel, asynchronous and randomized load coordination scheme called packetized energy management (PEM) is considered. Packetized energy management is a device-driven scheme that uses a unique request-response mechanism for coordinating diverse fleets of DERs. The aggregate dynamics of PEM are captured using state-bin transition models. Parameter heterogeneity is incorporated by grouping together relatively similar DERs. Furthermore, a notion of state of charge can be attached to the aggregate that is representative of the energy content in the fleet by means of a low order model. This low order model is of interest to the utilities and grid operators since it allows them to design control trajectories for DER aggregations depending upon grid requirements and load forecasts. Furthermore, a cyber-physical platform is developed for the validation of aggregate models and control schemes. However, PEM modifies the normal behavior of DERs and for accurate prediction of load dynamics, the underlying customer driven end-use process must be modeled to sufficient accuracy. Moreover, the modeled end-use process must be identifiable from the available data. In this work, the focus is on the uncontrollable hot-water extraction from the tank of an electric water heater. It is relevant and of interest to independent system operators (ISO) since water extraction is not usually measured and only metered interval consumption data (kWh) is collected. This is achieved by designing an estimation strategy based on a stochastic model of the end-use consumption

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