Guangchun Ruan

Massachusetts Institute of Technology | MIT

The slow convergence of existing distributed demand response methods is a general difficult problem. For acceleration, this paper proposes a new distributed method, namely the neural-network-based Lagrange multiplier selection (NN-LMS), to prominently reduce the iterations and avoid an oscillation. The key improvement lies in the forecast strategy...

With dramatic breakthroughs in recent years, machine learning is showing great potential to upgrade the toolbox for power system optimization. Understanding the strength and limitation of machine learning approaches is crucial to decide when and how to deploy them to boost the optimization performance. This paper pays special attention to the coord...

Due to heavy computational burdens, existing demand-side bidding models have to sacrifice the accuracy of uncertainty estimates in exchange for tractability, and therefore fail to derive a high bidding revenue as expected. To overcome this challenge, we analyze the entire bidding process, from scenario reduction to bidding curve construction, to fi...

Starting in early 2020, the novel coronavirus disease (COVID-19) severely affected the U.S., causing substantial changes in the operations of bulk power systems and electricity markets. In this paper, we develop a data-driven analysis to substantiate the pandemic's impacts from the perspectives of power system security, electric power generation, e...

There is an opportunity in modern power systems to explore the demand flexibility by incentivizing consumers with dynamic prices. In this paper, we quantify demand flexibility using an efficient tool called time-varying elasticity, whose value may change depending on the prices and decision dynamics. This tool is particularly useful for evaluating...

The battery performance and lifespan of electric vehicles (EVs) degrade significantly in cold climates, requiring a considerable amount of energy to heat up the EV batteries. This paper proposes a novel technology, namely temperature controlled smart charging, to coordinate the heating/charging power and reduce the energy use of a solar-powered EV...

Safe reinforcement learning (RL) is a popular and versatile paradigm to learn reward-maximizing policies with safety guarantees. Previous works tend to express the safety constraints in an expectation form due to the ease of implementation, but this turns out to be ineffective in maintaining safety constraints with high probability. To this end, we...

Frequency security of low-inertia power systems has become an increasing concern due to the high penetration of inverter-based resources. High-voltage direct current (HVDC) is a novel option for frequency regulation in multi-area asynchronous grids, and our focus is specifically on the primary frequency reserve sharing through HVDC. In this paper,...

In recent years, there has been a growing trend in the utilization of machine learning techniques for electricity theft detection. However, most existing works share a strong hypothesis that the dataset is sufficiently large (i.e., tens of thousands or more samples) to train effective models, which is not applicable to small sample cases commonly e...

Microgrid serves as a promising solution to integrate and manage distributed renewable energy resources. In this paper, we establish a stochastic multi-objective sizing optimization (SMOSO) model for microgrid planning, which fully captures the battery degradation characteristics and the total carbon emissions. The microgrid operator aims to simult...

Microgrid serves as a promising solution to integrate and manage distributed renewable energy resources. In this paper, we establish a stochastic multi-objective sizing optimization (SMOSO) model for microgrid planning, which fully captures the battery degradation characteristics and the total carbon emissions. The microgrid operator aims to simult...

For integrating heterogeneous distributed energy resources to provide fast frequency regulation, this paper proposes a dynamic virtual power plant~(DVPP) with frequency regulation capacity. A parameter anonymity-based approach is established for DVPP aggregating small-scaled inverter-based resources~(IBRs) with privacy concerns. On this basis, a pa...

Supervised machine learning models are receiving increasing attention in electricity theft detection due to their high detection accuracy. However, their performance depends on a massive amount of labeled training data, which comes from time-consuming and resource-intensive annotations. To maximize model performance within a limited annotation budg...

Modern network-constrained unit commitment (NCUC) bears a heavy computational burden due to the ever-growing model scale. This situation becomes more challenging when detailed operational characteristics, complicated constraints, and multiple objectives are considered. We propose a novel simplification method to determine the flexible temporal reso...

Reinforcement learning (RL) is a powerful tool for market agents solving decision-making problems in electricity markets. Vanilla RL enables agents to learn optimal policies in dynamic and uncertain market environments via trial and error. However, uncertainties in state transitions are often treated as exogenous state features with statistical err...

With the increasing concern about climate change, environmental pollution, and sustainable development, the energy system is evolving towards a low-carbon form powered by a large share of renewable energy. Renewable generation from wind and solar is intermittent and volatile, posing great challenges to the secure and economical operation of power s...

Aggregating distributed energy resources (DERs) is of great significance to improve the overall operational efficiency of smart grid. The aggregation model needs to consider various factors such as network constraints, operational constraints, and economic characteristics of the DERs. This paper constructs a multi-slot DER aggregation model that co...

The energy storage system (ESS) is a promising technology to address issues caused by the large‐scale deployment of renewable energy. Deploying ESS is a business decision that requires potential revenue assessment. Current value assessment methods focus on the energy storage owner or the electricity utility. The system value of the ESS needs to be...

Battery storage is essential to enhance the flexibility and reliability of electric power systems by providing auxiliary services and load shifting. Storage owners typically gains incentives from quick responses to auxiliary service prices, but frequent charging and discharging also reduce its lifetime. Therefore, this paper embeds the battery degr...

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...

With the increasing proportion of renewable energy in the generation side, it becomes more difficult to accurately predict the power generation and adapt to the large deviations between the optimal dispatch scheme and the day-ahead scheduling in the process of real-time dispatch. Therefore, it is necessary to conduct look-ahead dispatches to revise...

Decarbonization of power systems plays a crucial role in achieving carbon neutral goals across the globe, but there exists a sharp contradiction between the emission reduction and levelized generation cost. Therefore, it is of great importance for power system operators to take economic as well as low-carbon factors into account. This paper establi...

To address environmental challenges resulting from transportation sector's carbon emissions, replacing conventional internal combustion engine vehicles with electric vehicles is a key solution. Comprehensive electrification of heavy-duty trucks can promote the decarbonization of the freight transportation. The mismatch of supporting infrastructures...

The superior performance of deep learning relies heavily on a large collection of sample data, but the data insufficiency problem turns out to be relatively common in global electricity markets. How to prevent overfitting in this case becomes a fundamental challenge when training deep learning models in different market applications. With this in m...

The superior performance of deep learning relies heavily on a large collection of sample data, but the data insufficiency problem turns out to be relatively common in global electricity markets. How to prevent overfitting in this case becomes a fundamental challenge when training deep learning models in different market applications. With this in m...

Modern power systems are experiencing a variety of challenges driven by renewable energy, which calls for developing novel dispatch methods such as reinforcement learning (RL). Evaluation of these methods as well as the RL agents are largely under explored. In this paper, we propose an evaluation approach to analyze the performance of RL agents in...

This paper proposes a novel bi-level optimization model to study the strategic retail pricing and demand bidding problems of an electricity retailer that considers the interactions between demand response and market clearing process. In order to accurately forecast the day-ahead demand bids submitted by the retailer, a novel deep learning framework...

In electricity markets, locational marginal price (LMP) forecasting is particularly important for market participants in making reasonable bidding strategies, managing potential trading risks, and supporting efficient system planning and operation. Unlike existing methods that only consider LMPs' temporal features, this paper tailors a spectral gra...

Intervention policies against COVID-19 have caused large-scale disruptions globally, and led to a series of pattern changes in the power system operation. Analyzing these pandemic-induced patterns is imperative to identify the potential risks and impacts of this extreme event. With this purpose, we developed an open-access data hub (COVID-EMDA+), a...

Intervention policies against COVID-19 have caused large-scale disruptions globally, and led to a series of pattern changes in the power system operation. Analyzing these pandemic-induced patterns is imperative to identify the potential risks and impacts of this extreme event. With this purpose, we developed an open-access data hub (COVID-EMDA+), a...

Intervention policies against COVID-19 have caused large-scale disruptions globally, and led to a series of pattern changes in the power system operation. Analyzing these pandemic-induced patterns is imperative to identify the potential risks and impacts of this extreme event. With this purpose, we developed an open-access data hub (COVID-EMDA+), a...

Expansion planning for transmission networks and distribution networks has been widely investigated. For those entities that manage transmission and distribution assets, it is essential to develop a collaborative plan for both networks under coupling constraints, such as a total budget limitation constraint. Here, two technical issues should be con...

There is an opportunity in modern power systems to explore the demand flexibility by incentivizing consumers with dynamic prices. In this paper, we quantify demand flexibility using an efficient tool called time-varying elasticity, whose value may change depending on the prices and decision dynamics. This tool is particularly useful for evaluating...

In electricity markets, locational marginal price (LMP) forecasting is particularly important for market participants in making reasonable bidding strategies, managing potential trading risks, and supporting efficient system planning and operation. Unlike existing methods that only consider LMPs' temporal features, this paper tailors a spectral gra...

Starting in early 2020, the novel coronavirus disease (COVID-19) severely attached the U.S., causing substantial changes in the operations of bulk power systems and electricity markets. In this paper, we develop a data-driven analysis to substantiate the pandemic’s impacts from the perspectives of power system security, electric power generation, e...

Machine learning, with a dramatic breakthrough in recent years, is showing great potential to upgrade the power system optimization toolbox. Understanding the strength and limitation of machine learning approaches is crucial to answer when and how to integrate them in various power system optimization tasks. This paper pays special attention to the...

The novel coronavirus disease (COVID-19) has rapidly spread around the globe in 2020, with the US becoming the epicenter of COVID-19 cases since late March. As the US begins to gradually resume economic activity, it is imperative for policymakers and power system operators to take a scientific approach to understanding and predicting the impact on...

Starting in early 2020, the novel coronavirus disease (COVID-19) severely affected the U.S., causing substantial changes in the operations of bulk power systems and electricity markets. In this paper, we develop a data-driven analysis to substantiate the pandemic's impacts from the perspectives of power system security, electric power generation, e...

The novel coronavirus disease (COVID-19) has rapidly spread around the globe in 2020, with the U.S. becoming the epicenter of COVID-19 cases since late March. As the U.S. begins to gradually resume economic activity, it is imperative for policymakers and power system operators to take a scientific approach to understanding and predicting the impact...

The novel coronavirus disease (COVID-19) has rapidly spread around the globe in 2020, with the U.S. becoming the epicenter of COVID-19 cases and deaths in late March. In this context, there have been federal and state-level policy interventions aiming at mitigating the public health risks of this pandemic. These social distancing and work-from-home...

With the rapid growth of distributed energy resource integration, the power system is facing increased challenge of operation security. Demand response, as an underutilized resource, has shown great potential to support system security. This paper proposes a novel scheme to integrate heterogeneous demand side resources in the N-1 security assessmen...

Due to information asymmetry, analytical model may fail to keep high performance when some necessary information are absent. This paper provides a novel perspective to embed neural network (data-driven model) in optimization model (analytical model). The new-style model is then formulated and solved by a hybrid method with dual neural network and s...

Under the background of the untying of trading organization and the increasing scale of bilateral trading, aiming to deal with the conflict between the trading results of decentralized decision-making in the market and the system security, the idea of bilateral trading security pre-check is proposed in order to figure out the trading security regio...