Yi Wang

The University of Hong Kong | HKU · Department of Electrical and Electronic Engineering

Multiple energy systems (MES) bring together the electric power, heat, natural gas and other systems to improve the overall efficiency of the energy system. An Energy Hub (EH) models an MES as a device with multiple ports using a matrix coupling the inputs and outputs. This paper proposes a standardized matrix modeling method based on the concept o...

Distributed renewable energy, particularly photovoltaics (PV), has expanded rapidly over the past decade. Distributed PV is located behind the meter and is thus invisible to the retailers and the distribution system operator (DSO). This invisible generation thus injects additional uncertainty in the net load and makes it harder to forecast. This pa...

The widespread popularity of smart meters enables an immense amount of fine-grained electricity consumption data to be collected. Meanwhile, the deregulation of the power industry, particularly on the delivery side, has continuously been moving forward worldwide. How to employ massive smart meter data to promote and enhance the efficiency and susta...

The wide popularity of smart meters enables the collection of massive amounts of fine-grained electricity consumption data. Extracting typical electricity consumption patterns from these data supports the retailers in their understanding of consumer behaviors. In this way, diversified services such as personalized price design and demand response t...

The ubiquitous smart meters are expected to be a central feature of future smart grids because they enable the collection of massive amounts of fine-grained consumption data to support demand-side flexibility. However, current smart meters are not smart enough. They can only perform basic data collection and communication functions and cannot carry...

Having a better understanding of how locational marginal prices (LMPs) change helps in price forecasting and market strategy making. This paper investigates the fundamental distribution of the congestion part of LMPs in high-dimensional Euclidean space using an unsupervised approach. LMP models based on the lossless and lossy DC optimal power flow...

The proliferation of novel industrial applications at the wireless edge, such as smart grids and vehicle networks, demands the advancement of cyber-physical systems (CPSs). The performance of CPSs is closely linked to the last-mile wireless communication networks, which often become bottlenecks due to their inherent limited resources. Current CPS o...

Digitization is a prevailing trend in modern energy systems. With advancements in information and communications technology (ICT), advanced metering infrastructures, such as electric meters and gas meters, have been developed to record fine-grained energy consumption data. As a result, an increasing amount of data can now be accessed and collected,...

The growing penetration of electric vehicles (EVs) significantly changes typical load curves in smart grids. With the development of fast charging technology, the volatility of EV charging demand is increasing, which requires additional flexibility for real-time power balance. The forecasting of EV charging demand involves probabilistic modeling of...

Non-intrusive load monitoring (NILM) is a critical technology for disaggregating appliance-specific energy usage by only observing household-level power consumption. If NILM can be performed on end devices (such as smart meters), it can facilitate electricity demand identification and electricity behavior perception for real-time demand-side energy...

Time series analysis is widely used in many fields such as power energy, economics, and transportation, including different tasks such as forecasting, anomaly detection, classification, etc. Missing values are widely observed in these tasks, and often leading to unpredictable negative effects on existing methods, hindering their further application...

Complex distribution shifts are the main obstacle to achieving accurate long-term time series forecasting. Several efforts have been conducted to capture the distribution characteristics and propose adaptive normalization techniques to alleviate the influence of distribution shifts. However, these methods neglect the intricate distribution dynamics...

Prosumers with flexible distributed energy resources (DERs) can be aggregated as a virtual power plant (VPP) to participate in the electricity market. However, the VPP’s energy management requires the prosumers to share individual data, which poses privacy concerns. This paper proposes a distributed differentially private energy management strategy...

Electrical load forecasting plays a crucial role in decision-making for power systems. The integration of renewable energy sources and the occurrence of external events, such as the COVID-19 pandemic, have rapidly increased uncertainties in load forecasting. The uncertainties in load forecasting can be divided into two types: epistemic uncertainty...

Forecasting is pivotal in energy systems, by providing fundamentals for operation at different horizons and resolutions. Though energy forecasting has been widely studied for capturing temporal information, very few works concentrate on the frequency information provided by forecasts. They are consequently often limited to single-resolution applica...

Data-driven OPF has been widely studied recently to satisfy the real-time requirements of applications like economic dispatch, security analysis, etc. However, traditional data-driven models are typically trained for a specific system topology. When the system topology changes, the models must either be retrained (which demands a substantial amount...

Load forecasting plays a vital role in achieving supply-demand balance in power systems and lays the foundation for economic dispatch, demand response, etc. Conventionally, a global forecasting model will be constructed for the whole dataset. The problem of non-independently and identically distributed (non-i.i.d) data causes performance degradatio...

To enhance the quality of energy management tasks, accurately representing the thermal dynamics of buildings is crucial. Traditional methods aim to improve the building model in regards to an arbitrary statistical metric, before feeding the trained model to the optimization-based energy management process. In this paper, we advocate for a more inte...

Transitioning towards a low-carbon future necessitates massive efforts from both the transport and power sectors. Electric vehicles (EVs) have emerged as a promising approach to realize this objective, leveraging their smart routing strategies and vehicle-to-grid (V2G) techniques. Previous studies have addressed various challenges in EV routing and...

Renewable-based standalone systems are widely developed worldwide with the decentralization of power and energy systems. However, challenges are posed due to the intermittent nature of renewable resources and the lack of inertia. Small modular reactors (SMRs), a clean but also flexible and controllable energy, can be deployed to provide flexibility...

Household load forecasting is increasingly essential since it enables various demand-side management applications. The federated learning approach is becoming popular for its advantages in fully using different households’ load data with privacy preservation. However, due to the non-independent and identically distributed (non-IID) characteristic o...

The increasing renewable penetration leads to a great need for flexible resources to balance supply and demand. Building energy systems can provide considerable flexibility by optimally coordinating various appliances. However, the complex thermodynamics and insufficient data associated challenge the modeling and operation of building energy system...

The study of time series data is crucial for understanding trends and anomalies over time, enabling predictive insights across various sectors. Spatio-temporal data, on the other hand, is vital for analyzing phenomena in both space and time, providing a dynamic perspective on complex system interactions. Recently, diffusion models have seen widespr...

Accurate load forecasting serves as the foundation for the flexible operation of multi-energy systems (MES). Multi-energy loads are tightly coupled and exhibit significant uncertainties. Many works focus on enhancing forecasting accuracy by leveraging cross-sector information. However, data owners may not be motivated to share their data unless it...

The ubiquitous smart meters are expected to be a central feature of future smart grids by enabling the collection of massive fine-grained consumption data to support demand-side flexibility. However, the current smart meters are still not smart enough. They can only perform basic data collection and communication functionalities but fail to carry o...

Solving AC-optimal power flow (AC-OPF) in real-time is crucial for further power system operation and security analysis. To this end, data-driven methods are employed to directly output the OPF solution. However, due to the prediction error, it is a challenge for data-driven methods to provide a feasible solution. To address this issue, different f...

With the increasing numbers of smart meter installations, scalable and efficient load forecasting techniques are critically needed to ensure sustainable situation awareness within the distribution networks. Distribution networks include a large amount of different loads at various aggregation levels, such as individual consumers, low-voltage feeder...

Temporal data, notably time series and spatio-temporal data, are prevalent in real-world applications. They capture dynamic system measurements and are produced in vast quantities by both physical and virtual sensors. Analyzing these data types is vital to harnessing the rich information they encompass and thus benefits a wide range of downstream t...

Accurate mid-term gas demand forecasting plays a crucial role for gas companies and policymakers to achieve reliable gas supply plans, supply contracts management, and efficient operation to meet the increasing gas demand. However, mid-term gas demand forecasting faces the problems of data paucity caused by the low frequency of collecting monthly d...

As renewable generation becomes more prevalent, traditional power systems dominated by synchronous generators are transitioning to systems dominated by converter-interfaced generation. These devices, with their weaker damping capabilities and lower inertia, compromise the system's ability to withstand disturbances, pose a threat to system stability...

Urban areas account for more than 70% of total carbon emissions. This proportion for China is higher, approximately 80%, closely related to human activities in urban energy consumption. In this context, the low-carbon transition of urban energy systems has become an important strategic goal for China to deal with climate change and seek sustainable...

Wind energy is one of the most significant renewable sources of energy while accurate and reliable wind power forecasting methods may greatly benefit power system planning and scheduling. Recently, many machine learning algorithms have shown significant advantages in how to extract temporal features for wind power forecasting. However, wind power c...

Load forecasting is of great significance in the power industry as it can provide a reference for subsequent tasks such as power grid dispatch, thus bringing huge economic benefits. However, there are many differences between load forecasting and traditional time series forecasting. On the one hand, load forecasting aims to minimize the cost of sub...

An increasing number of distributed energy resources (DERs), such as rooftop photovoltaic (PV), electric vehicles (EVs), distributed energy storage, etc., are being integrated into the distribution systems. The rise of DERs has come hand-in-hand with large amounts of data generated and explosive growth in data collection, communication, and control...

The growth of variable renewable generations will reduce synchronous inertia, and the demand for managing contingency frequency support is growing. Distributed energy resources (DERs) can provide contingency frequency support via the virtual power plant (VPP). Given that the VPP may cover thousands of DERs with diverse characteristics and response...

Electrical load forecasting is of great significance for the decision makings in power systems, such as unit commitment and energy management. In recent years, various self-supervised neural network-based methods have been applied to electrical load forecasting to improve forecasting accuracy and capture uncertainties. However, most current methods...

With increasing distributed energy resource integration, future power and energy systems will be more decentralized using advanced Internet of Things (IoT) technologies. Integrated energy systems (IES) boost the whole energy efficiency by coordinating multi-regional energy resources and networks. However, distributed coordination of the IES require...

Higher accurate load forecasts help the power system operator make better resource allocation and reduce operational costs. Ensemble learning has been widely used to improve the accuracy of final forecasts by combining multiple individual forecasts. In the digital economy era, the system operator can buy high-quality load forecasts from the data ma...

Uncertain distributed energy resources and uneven load allocation cause the three-phase unbalance in distribution networks (DNs), which may harm the health of power equipment and increase the operational cost. There are emerging opportunities to balance three-phase DNs with a number of power electronic devices installed in the system. In this paper...

Load forecasting techniques have been well developed and almost all typical models are trying to minimize the error over all equally weighted data samples. However, treating all data equally in the training process is unreasonable and even harmful to model generalization. This letter investigates the issue of data inequality which is crucial but is...

Long‐term storage will play a crucial role in future local multi‐energy systems (MES) with high penetration renewable energy integration for demand balancing. Local MES planning with long‐term energy storage is essentially a very large‐scale program because numerous decision variables, including binary variables, should be used to model long‐term e...

We use symmetric cryptography for secure communications with resource limited smart grid control devices. We propose the novel idea of using the unmanned aerial vehicle (UAV) as a physical courier to carry secret key bits generated at the control center to remote control devices. While distributing secret keys, the UAV may be attacked in an attempt...

Timely and high-density air quality monitoring is essential for the development of future smart cities. The images captured from widely deployed stationary-cameras can be transferred quickly via the Internet of Things (IoT) to facilitate ambient pollution estimation anytime anywhere. Image-based air pollution estimation is normally formulated as a...

Big data has been advocated as a dominant driving force to unleash the great waves of the next-generation industrial revolution. While the ever-increasing proliferation of heterogeneous data contributes to a more sustainable energy system, considerable challenges remain for breaking down the barrier of data sharing across monopolistic sectors and f...

Building-level load forecasting is becoming increasingly crucial since it forms the foundation for better building energy management, which will lower energy consumption and reduce CO$_2$ emissions. However, building-level load forecasting faces the challenges of high load volatility and heterogeneous consumption behaviors. Simple regression models...

Better understanding the behavior of various participants in smart grids, such as electricity consumers and generators, is important and beneficial for flexibility exploration and renewable energy accommodation. Clustering, as an effective data-driven approach to behavior analysis, has been widely applied for extracting the typical electricity cons...

The growing penetration of electric vehicles (EVs) significantly changes typical load curves in smart grids. With the development of fast charging technology, the volatility of EV charging demand is increasing, which requires additional flexibility for real-time power balance. The forecasting of EV charging demand involves probabilistic modeling of...

The virtual power plant (VPP) has the potential to provide frequency regulation services by aggregating demand-side distributed energy resources (DERs). Cellular communication networks are commonly utilized for connecting DERs. Nevertheless, conventional cellular networks prior to 5G were unreliable, while private cellular networks incur huge inves...

Building-level load forecasting has become essential with the support of fine-grained data collected by widely deployed smart meters. It acts as a basis for arranging distributed energy resources, implementing demand response, etc. Compared to aggregated-level load, the electric load of an individual building is more stochastic and thus spawns many...

With increasing numbers of prosumers employed with multi-energy systems (MES) towards higher energy utilization efficiency, an advanced energy management scheme is becoming increasingly important. The incorporation of MES into the existential energy market holds promise for future power systems. The continuous double auction (CDA) market, in a dece...

The rapid development of distributed photovoltaic (PV) systems poses great challenges to the integration capability of distribution networks. Traditionally, the transfer capacity of power distribution equipment is calculated as the maximum loading that prevents overheating under the assumption of extreme weather conditions. Dynamic thermal rating (...

Real-time electricity prices are economic signals incentivizing market players to support real-time system balancing. These price signals typically switch between low- and high-price regimes depending on whether the power system is in surplus or shortage of generation, which is hard to capture. In this context, we propose a new Transformer-based mo...

Electric load forecasting is an essential problem for the power industry, which has a significant impact on power system operation. Currently, deep learning is proved to be an effective tool for load forecasting. However, those learning-based models are vulnerable towards adversarial attacks, which raises concerns about the robustness of load forec...

In the original version of the book, the following belated corrections have been made

The COVID-19 related shutdowns have made significant impacts on the electric grid operation worldwide. The global electrical demand plummeted around the planet in 2020 continuing into 2021. Moreover, demand shape has been profoundly altered as a result of industry shutdowns, business closures, and people working from home. In view of such massive e...

Nowadays, multi-energy systems are receiving special attention from smart grid community owing to their high flexibility potentials integrating with multiple energy carriers. In this regard, energy hub is known as a flexible and efficient platform to supply energy demands with an acceptable range of affordability and reliability by relying on vario...

The current wind farm control schemes qualify wind power producers (WPPs) to provide balancing services in complement to energy in modern electricity markets. Accordingly, WPPs are responsible for real-time deviations in both energy and reserve market floors, which are settled at different time scales. WPPs should adjust their output to cope with f...

The installation of smart meters enables electricity retailers or consumers to implement individual load forecasting for demand response. An individual load forecasting model can be trained either on each consumer's own smart meter data or the smart meter data of multiple consumers. The former practice may suffer from overfitting if a complex model...

For electricity market participants, proper information on the current grid topology is helpful for applications such as locational marginal price (LMP)/congestion forecasting, valuation of electricity derivatives, and operation of generation/distribution assets. However, in many markets, the publication of the grid topology is usually untimely or...

COVID-19-related shutdowns have significantly impacted the electrical grid operation worldwide, as governments put strict measures in place to manage the global pandemic. The global electrical demand plummeted around the planet in March, April, and May 2020, with countries such as Spain and Italy experiencing more than 20% decrease in their usual e...

This paper presents a new privacy-preserving framework for the short-term (multi-horizon) probabilistic forecasting of nodal voltages in local energy communities. This task is indeed becoming increasingly important for cost-effectively managing network constraints in the context of the massive integration of distributed energy resources. However, t...

Accurate load prediction is an effective way to reduce power system operation costs. Traditionally, the Mean Square Error (MSE) is a common-used loss function to guide the training of an accurate load forecasting model. However, the MSE loss function is unable to precisely reflect the real costs associated with forecasting errors because the cost c...

In virtual power plants (VPPs), distributed devices (such as residential appliances, energy storage, and electric vehicles) communicate with control centers via wireless access points (APs). Nevertheless, with a massive number of devices connected to APs, VPPs would be adversely affected by packet loss and consequently suffer from revenue reduction...

Probabilistic wind power forecasting is an important input in the decision-making process in future electric power grids with large penetrations of renewable generation. Traditional probabilistic wind power forecasting models are trained offline and are then used to make predictions online. However, this strategy cannot make full use of the most re...

The operations of base stations (BSs) contribute most of the energy consumption in the cellular wireless networks. Powering BSs by distributed energy resources (DER) such as photovoltaic (PV) and energy storage is an effective way to reduce on-grid power consumption and build green wireless networks. Optimal energy management of BSs helps to reduce...

Smart meters (SMs) measure and transmit fine-grained electricity consumption data to the data center at a certain high frequency (such as every 15 minutes). A data aggregation point (DAP) is a relay device between a cluster of SMs and data centers. The placement of DAPs significantly impacts the cost-effectiveness and quality of service (QoS) of th...

Massive and various bad data may be introduced to load profiles in the process of data acquisition, transmission, and storage deliberately or accidentally due to cyber attacks and equipment failures. The bad data may result in bias for load forecasting and other data analytic applications. This chapter proposes a novel bad data identification and r...

Myriad studies have been conducted on bidding behaviors following a worldwide restructuring of the electric power market. The common theme in such studies involves idealized and theoretical economic assumptions. However, practical bidding behaviors could deviate from that based on theoretical assumptions, which would undoubtedly limit the effective...

One of the key steps for optimal bidding in power markets is to estimate the rivals’ bidding behaviors. However, for most participants, it would be difficult to directly forecast the rivals’ individual bids due to the information privacy and volatile characteristics of individual bidding behaviors. From another point of view, the aggregation of ind...

Short-term locational marginal price (LMP) forecasting is the traditional problem of market participants and other institutions maximizing their profit. Most electricity market organizers in the world release the data of LMP along with its three components, i.e., the energy, congestion, and loss components. The series of the three components have t...

Economic growth has greatly fluctuated around the world in recent years, and external economic factors (EEFs) have imposed more obvious effects on electricity consumption. To improve the accuracy and applicability of mid-term, especially monthly, electricity consumption forecasting, a novel monthly electricity consumption forecasting framework (den...

Due to the restructuring of power markets worldwide, market simulation methods have attracted increasing attention. To address the limitations of the current commonly used methods, which are equilibrium analysis and agent-based simulation, a data-driven bottom-up power market simulation framework is proposed based on learning from individual offeri...

Having a better understanding of how locational marginal prices (LMPs) change helps in price forecasting and market strategy making. This chapter investigates the fundamental distribution of the congestion part of LMPs in high-dimensional Euclidean space using an unsupervised approach. LMP models based on the lossless and lossy DC optimal power flo...

Probabilistic load forecasting (PLF) has been extensively studied recently to characterize the uncertainties of future loads. Traditional PLF is implemented based on the historical load data itself and other relevant factors. However, the prevalence of smart meters provides more fine-grained consumption information. This chapter proposes a novel pr...

Probabilistic load forecasting (PLF) is able to present the uncertainty information of the future loads. It is the basis of stochastic power system planning and operation. Recent works on PLF mainly focus on how to develop and combine forecasting models; while the feature selection issue has not been thoroughly investigated for PLF. This chapter fi...

The market deregulation of the power industry based on the principle of “bid-based, security-constrained economic dispatch (SCED)" has resulted in dramatic changes for system operators, generation companies, and electricity consumers. The operation of power markets constantly produces valuable market data which can support the decision of both mark...

Due to the deregulation of power systems worldwide, bidding behavior simulation research has gained prominence. One crucial element in these studies is accurately defining the individual reward function (or objective function). Considering the information barriers between market participants and researchers, the common way is to develop reward func...