arXiv subjects

Ye Guo

Publications and source records attributed to Ye Guo.

At least 37 records · Page 2Linked to original sources

arXiv2024

Nonparametric End-to-End Probabilistic Forecasting of Distributed Generation Outputs Considering Missing Data Imputation

In this paper, we introduce a nonparametric end-to-end method for probabilistic forecasting of distributed renewable generation outputs while including missing data imputation. Firstly, we employ a nonparametric probabilistic forecast model utilizing the long short-term memory (LSTM) network to model the probability distributions of distributed renewable generations' outputs. Secondly, we design an end-to-end training process that includes missing data imputation through iterative imputation and iterative loss-based training procedures. This two-step modeling approach effectively combines the strengths of the nonparametric method with the end-to-end approach. Consequently, our approach demonstrates exceptional capabilities in probabilistic forecasting for the outputs of distributed renewable generations while effectively handling missing values. Simulation results confirm the superior performance of our approach compared to existing alternatives.

eess.SY↗

arXiv2024

Enhancing Continuous Domain Adaptation with Multi-Path Transfer Curriculum

Addressing the large distribution gap between training and testing data has long been a challenge in machine learning, giving rise to fields such as transfer learning and domain adaptation. Recently, Continuous Domain Adaptation (CDA) has emerged as an effective technique, closing this gap by utilizing a series of intermediate domains. This paper contributes a novel CDA method, W-MPOT, which rigorously addresses the domain ordering and error accumulation problems overlooked by previous studies. Specifically, we construct a transfer curriculum over the source and intermediate domains based on Wasserstein distance, motivated by theoretical analysis of CDA. Then we transfer the source model to the target domain through multiple valid paths in the curriculum using a modified version of continuous optimal transport. A bidirectional path consistency constraint is introduced to mitigate the impact of accumulated mapping errors during continuous transfer. We extensively evaluate W-MPOT on multiple datasets, achieving up to 54.1\% accuracy improvement on multi-session Alzheimer MR image classification and 94.7\% MSE reduction on battery capacity estimation.

cs.LG↗

arXiv2023

Integrating Renewable Energy Sources as Reserve Providers: Modeling, Pricing, and Properties

In pursuit of carbon neutrality, many countries have adopted renewable portfolio standards to facilitate the integration of renewable energy. However, increasing penetration of renewable energy resources will also pose higher requirements on system flexibility. Allowing renewable themselves to participate in the reserve market could be a viable solution. To this end, this paper proposes an optimal dispatch model for joint energy-reserve procurement that incorporates renewable portfolio standards and RES serve as reserve providers. Potential generator outages and deviations in renewable and load power are modelled through a given number of probability-weighted scenarios. In particular, reserve resources are initially booked in the base case and then activated in non-base scenarios through the re-dispatch process. Marginal pricing is used to derive energy, reserve, and power deviation prices. Next, we develop the associated settlement process and establish several market properties. The proposed pricing scheme establishes equivalence between thermal generators and renewable units by accounting for their uncertainties, including thermal generator outages and renewable power deviations, and their flexibility, namely reserve and re-dispatch. We have shown that for renewable resources, supplying reserve according to the dispatch results compared to generating as much as possible leads to better profits. Simulations validate the effectiveness of the proposed method and properties established.

eess.SY↗

arXiv2023

Dynamic Operating Envelopes Embedded Peer-to-Peer-to-Grid Energy Trading

A novel decentralized peer-to-peer-to-grid (P2P2G) trading mechanism considering distribution network integrity is proposed. In order to direct prosumers' peer-to-peer (P2P) trading behavior to be grid-friendly, the proposed method incorporates Dynamic Operating Envelopes (DOEs) into the existing P2P2G trading. Moreover, DOEs are determined through negotiations between the distribution system operator (DSO) and prosumers alongside the process of P2P trading, avoiding compromising prosumers' privacy and network parameters leakage. To reduce communication costs during P2P trading, a variant of the alternating direction method of multipliers (ADMM), i.e., communication-censored ADMM (COCA) is used to solve the P2P2G trading problem. Finally, the DOE price is shown to be comprised of several economically interpretable components. Simulations validate the effectiveness of the proposed mechanism.

eess.SY↗

arXiv2023

Improving Vision-and-Language Reasoning via Spatial Relations Modeling

Visual commonsense reasoning (VCR) is a challenging multi-modal task, which requires high-level cognition and commonsense reasoning ability about the real world. In recent years, large-scale pre-training approaches have been developed and promoted the state-of-the-art performance of VCR. However, the existing approaches almost employ the BERT-like objectives to learn multi-modal representations. These objectives motivated from the text-domain are insufficient for the excavation on the complex scenario of visual modality. Most importantly, the spatial distribution of the visual objects is basically neglected. To address the above issue, we propose to construct the spatial relation graph based on the given visual scenario. Further, we design two pre-training tasks named object position regression (OPR) and spatial relation classification (SRC) to learn to reconstruct the spatial relation graph respectively. Quantitative analysis suggests that the proposed method can guide the representations to maintain more spatial context and facilitate the attention on the essential visual regions for reasoning. We achieve the state-of-the-art results on VCR and two other vision-and-language reasoning tasks VQA, and NLVR.

cs.CV↗

arXiv2023

On Degeneracy Issues in Multi-parametric Programming and Critical Region Exploration based Distributed Optimization in Smart Grid Operations

Improving renewable energy resource utilization efficiency is crucial to reducing carbon emissions, and multi-parametric programming has provided a systematic perspective in conducting analysis and optimization toward this goal in smart grid operations. This paper focuses on two aspects of interest related to multi-parametric linear/quadratic programming (mpLP/QP). First, we study degeneracy issues of mpLP/QP. A novel approach to deal with degeneracies is proposed to find all critical regions containing the given parameter. Our method leverages properties of the multi-parametric linear complementary problem, vertex searching technique, and complementary basis enumeration. Second, an improved critical region exploration (CRE) method to solve distributed LP/QP is proposed under a general mpLP/QP-based formulation. The improved CRE incorporates the proposed approach to handle degeneracies. A cutting plane update and an adaptive stepsize scheme are also integrated to accelerate convergence under different problem settings. The computational efficiency is verified on multi-area tie-line scheduling problems with various testing benchmarks and initial states.

eess.SY↗

arXiv2023

An Adaptive Approach for Probabilistic Wind Power Forecasting Based on Meta-Learning

This paper studies an adaptive approach for probabilistic wind power forecasting (WPF) including offline and online learning procedures. In the offline learning stage, a base forecast model is trained via inner and outer loop updates of meta-learning, which endows the base forecast model with excellent adaptability to different forecast tasks, i.e., probabilistic WPF with different lead times or locations. In the online learning stage, the base forecast model is applied to online forecasting combined with incremental learning techniques. On this basis, the online forecast takes full advantage of recent information and the adaptability of the base forecast model. Two applications are developed based on our proposed approach concerning forecasting with different lead times (temporal adaptation) and forecasting for newly established wind farms (spatial adaptation), respectively. Numerical tests were conducted on real-world wind power data sets. Simulation results validate the advantages in adaptivity of the proposed methods compared with existing alternatives.

eess.SY↗

arXiv2023

Multi-Interval Rolling-Window Joint Dispatch and Pricing of Energy and Reserve under Uncertainty

In this paper, the intra-day multi-interval rolling-window joint dispatch and pricing of energy and reserve is studied under increasing volatile and uncertain renewable generations. A look-ahead energy-reserve co-optimization model is proposed for the rolling-window dispatch, where possible contingencies and load/renewable forecast errors over the look-ahead window are modeled as several scenario trajectories, while generation, especially its ramp, is jointly scheduled with reserve to minimize the expected system cost considering these scenarios. Based on the proposed model, marginal prices of energy and reserve are derived, which incorporate shadow prices of generators' individual ramping capability limits to eliminate their possible ramping-induced opportunity costs or arbitrages. We prove that under mild conditions, the proposed market design provides dispatch-following incentives to generators without the need for out-of-the-market uplifts, and truthful-bidding incentives of price-taking generators can be guaranteed as well. Some discussions are also made on how to fit the proposed framework into current market practice. These findings are validated in numerical simulations.

eess.SY↗

arXiv2023

Distributed Dual Subgradient Methods with Averaging and Applications to Grid Optimization

We study finite-time performance of a recently proposed distributed dual subgradient (DDSG) method for convex constrained multi-agent optimization problems. The algorithm enjoys performance guarantees on the last primal iterate, as opposed to those derived for ergodic means for vanilla DDSG algorithms. Our work improves the recently published convergence rate of $\Ocal(\log T/\sqrt{T})$ with decaying step-sizes to $\Ocal(1/\sqrt{T})$ with constant step-size on a metric that combines suboptimality and constraint violation. We then numerically evaluate the algorithm on three grid optimization problems. Namely, these are tie-line scheduling in multi-area power systems, coordination of distributed energy resources in radial distribution networks, and joint dispatch of transmission and distribution assets. The DDSG algorithm applies to each problem with various relaxations and linearizations of the power flow equations. The numerical experiments illustrate various properties of the DDSG algorithm--comparison with vanilla DDSG, impact of the number of agents, and why Nesterov-style acceleration fails in DDSG settings.

math.OC↗

arXiv2023

Smart Meters Integration in Distribution System State Estimation with Collaborative Filtering and Deep Gaussian Process

The problem of state estimations for electric distribution system is considered. A collaborative filtering approach is proposed in this paper to integrate the slow time-scale smart meter measurements in the distribution system state estimation, in which the deep Gaussian process is incorporated to infer the fast time-scale pseudo measurements and avoid anomalies. Numerical tests have demonstrated the higher estimation accuracy of the proposed method.

eess.SY↗

arXiv2022

Appendix for Nonparametric Multivariate Probability Density Forecast in Smart Grids With Deep Learning

This paper proposes a nonparametric multivariate density forecast model based on deep learning. It not only offers the whole marginal distribution of each random variable in forecasting targets, but also reveals the future correlation between them. Differing from existing multivariate density forecast models, the proposed method requires no a priori hypotheses on the forecasted joint probability distribution of forecasting targets. In addition, based on the universal approximation capability of neural networks, the real joint cumulative distribution functions of forecasting targets are well-approximated by a special positive-weighted deep neural network in the proposed method. Numerical tests from different scenarios were implemented under a comprehensive verification framework for evaluation, including the very short-term forecast of the wind speed, wind power, and the day-ahead forecast of the aggregated electricity load. Testing results corroborate the superiority of the proposed method over current multivariate density forecast models considering the accordance with reality, prediction interval width, and correlations between different random variables.

eess.SY↗

arXiv2022

Reducing Action Space: Reference-Model-Assisted Deep Reinforcement Learning for Inverter-based Volt-Var Control

Reference-model-assisted deep reinforcement learning (DRL) for inverter-based Volt-Var Control (IB-VVC) in active distribution networks is proposed. We investigate that a large action space increases the learning difficulties of DRL and degrades the optimization performance in the process of generating data and training neural networks. To reduce the action space of DRL, we design a reference-model-assisted DRL approach. We introduce definitions of the reference model, reference-model-based optimization, and reference actions. The reference-model-assisted DRL learns the residual actions between the reference actions and optimal actions, rather than learning the optimal actions directly. Since the residual actions are considerably smaller than the optimal actions for a reference model, we can design a smaller action space for the reference-model-assisted DRL. It reduces the learning difficulties of DRL and optimises the performance of the reference-model-assisted DRL approach. It is noteworthy that the reference-model-assisted DRL approach is compatible with any policy gradient DRL algorithms for continuous action problems. This work takes the soft actor-critic algorithm as an example and designs a reference-model-assisted soft actor-critic algorithm. Simulations show that 1) large action space degrades the performance of DRL in the whole training stage, and 2) reference-model-assisted DRL requires fewer iteration times and returns a better optimization performance.

eess.SY↗

arXiv2022

Energy-Grade Double Pricing Rule in the Heating Market

The problem of heat system pricing is considered. A direct extension of locational marginal prices (LMP) in electricity markets to heat systems may lead to revenue inadequate issues. The underlying reason for such a problem is that, unlike electric power, heat has different grades and cannot be considered as homogenized commodity. Accordingly, an energy-grade double pricing rule is proposed in this paper. Heat energy and grade prices are explained as the shadow prices related to the nodal heat balance constraints and temperature requirements constraints at the optimal solution. The resulting merchandise surplus at each dispatch interval can be decomposed into several explainable parts, namely, congestion rent, impact from the last period, and impact from the upcoming period. And the total merchandise surplus over all dispatch intervals can be decomposed into several non-negative interpretable parts, including congestion rent and impact from the initial state, thus guaranteeing the revenue adequacy for the heat system operator. Simulations verify the effectiveness of the proposed mechanism.

eess.SY↗

arXiv2022

Energy-grade double pricing mechanism for a combined heat and power system using the asynchronous dispatch method

The problem of heat and electricity pricing in combined heat and power systems regarding the time scales of electricity and heat, as well as thermal energy quality, is studied. Based on the asynchronous coordinated dispatch of the combined heat and power system, an energy-grade double pricing mechanism is proposed. Under the pricing mechanism, the resulting merchandise surplus of the heat system operator at each heat dispatch interval can be decomposed into interpretable parts and its revenue adequacy can be guaranteed for all heat dispatch intervals. And the electric power system operator's resulting merchandise surplus is composed of non-negative components at each electricity dispatch interval, also ensuring its revenue adequacy. In addition, the effects of different time scales and cogeneration are analyzed in different kinds of combined heat and power units' pricing.

eess.SY↗

arXiv2022

A Multiple Market Trading Mechanism for Electricity, Renewable Energy Certificate and Carbon Emission Right of Virtual Power Plants

A multiple market trading mechanism for the VPP to participate in electricity, renewable energy certificate (REC) and carbon emission right (CER) markets is proposed. With the introduction of the inventory mechanism of REC and CER, the profit of the VPP increases and better trading decisions with multiple markets are made under the requirements of renewable portfolio standard (RPS) and carbon emission (CE) quota requirements. According to the Karush-Kuhn-Tucker (KKT) conditions of the proposed model, properties of the multiple market trading mechanism are discussed. Results from case studies verify the effectiveness of the proposed model.

eess.SY↗

arXiv2022

MPC-Based Operation Strategy for Electric Vehicle Aggregators Considering Regulation Markets

The optimal operation problem of electric vehicle aggregator (EVA) is considered. An EVA can participate in energy and regulation markets with its current and upcoming EVs, thus reducing its total cost of purchasing energy to fulfill EVs' charging requirements. A model predictive control (MPC) based optimization is developed to consider the future arrival of EVs as well as energy and regulation prices. The index of conditional value-at-risk (CVaR) is used to model the risk-averseness of an EVA. Simulations on a 2000-EV test system validate the effectiveness of our work in achieving a lucrative revenue while satisfying the charging requests from EV owners.

eess.SY↗

arXiv2022

Reducing Learning Difficulties: One-Step Two-Critic Deep Reinforcement Learning for Inverter-based Volt-Var Control

A one-step two-critic deep reinforcement learning (OSTC-DRL) approach for inverter-based volt-var control (IB-VVC) in active distribution networks is proposed in this paper. Firstly, considering IB-VVC can be formulated as a single-period optimization problem, we formulate the IB-VVC as a one-step Markov decision process rather than the standard Markov decision process, which simplifies the DRL learning task. Then we design the one-step actor-critic DRL scheme which is a simplified version of recent DRL algorithms, and it avoids the issue of Q value overestimation successfully. Furthermore, considering two objectives of VVC: minimizing power loss and eliminating voltage violation, we utilize two critics to approximate the rewards of two objectives separately. It simplifies the approximation tasks of each critic, and avoids the interaction effect between two objectives in the learning process of critic. The OSTC-DRL approach integrates the one-step actor-critic DRL scheme and the two-critic technology. Based on the OSTC-DRL, we design two centralized DRL algorithms. Further, we extend the OSTC-DRL to multi-agent OSTC-DRL for decentralized IB-VVC and design two multi-agent DRL algorithms. Simulations demonstrate that the proposed OSTC-DRL has a faster convergence rate and a better control performance, and the multi-agent OSTC-DRL works well for decentralized IB-VVC problems.

cs.AI↗

arXiv2022

Distributed Multi-Area Optimal Power Flow via Rotated Coordinate Descent Critical Region Exploration

We consider the problem of distributed optimal power flow (OPF) for multi-area electric power systems. A novel distributed algorithm is proposed, referred to as the rotated coordinate descent critical region exploration (RCDCRE). It allows each entity to independently update its boundary information and optimally solve its local OPF in an asynchronous fashion. RCDCRE method stitches coordinate descent and parametric programming using coordinate system rotation to reduce coordination, keep privacy and ensure convergence. The solution process does not require warm starts and can iterate from infeasible initial points using penalty-based formulations. The effectiveness of RCDCRE is verified based on IEEE 2-area 44-bus and 4-area 472-bus systems.

math.OC↗