arXiv subjects

Ziqing Zhu

Publications and source records attributed to Ziqing Zhu.

25 records · Page 2Linked to original sources

arXiv2023

How to Use Reinforcement Learning to Facilitate Future Electricity Market Design? Part 1: A Paradigmatic Theory

In face of the pressing need of decarbonization in the power sector, the re-design of electricity market is necessary as a Marco-level approach to accommodate the high penetration of renewable generations, and to achieve power system operation security, economic efficiency, and environmental friendliness. However, existing market design methodologies suffer from the lack of coordination among energy spot market (ESM), ancillary service market (ASM) and financial market (FM), i.e., the "joint market", and the lack of reliable simulation-based verification. To tackle these deficiencies, this two-part paper develops a paradigmatic theory and detailed methods of the joint market design using reinforcement-learning (RL)-based simulation. In Part 1, the theory and framework of this novel market design philosophy are proposed. First, the controversial market design options while designing the joint market are summarized as the targeted research questions. Second, the Markov game model is developed to describe the bidding game in the joint market, incorporating the market design options to be determined. Third, a framework of deploying multiple types of RL algorithms to simulate the market model is developed. Finally, several market operation performance indicators are proposed to validate the market design based on the simulation results.

cs.AI↗

arXiv2023

How to Use Reinforcement Learning to Facilitate Future Electricity Market Design? Part 2: Method and Applications

This two-part paper develops a paradigmatic theory and detailed methods of the joint electricity market design using reinforcement-learning (RL)-based simulation. In Part 2, this theory is further demonstrated by elaborating detailed methods of designing an electricity spot market (ESM), together with a reserved capacity product (RC) in the ancillary service market (ASM) and a virtual bidding (VB) product in the financial market (FM). Following the theory proposed in Part 1, firstly, market design options in the joint market are specified. Then, the Markov game model is developed, in which we show how to incorporate market design options and uncertain risks in model formulation. A multi-agent policy proximal optimization (MAPPO) algorithm is elaborated, as a practical implementation of the generalized market simulation method developed in Part 1. Finally, the case study demonstrates how to pick the best market design options by using some of the market operation performance indicators proposed in Part 1, based on the simulation results generated by implementing the MAPPO algorithm. The impacts of different market design options on market participants' bidding strategy preference are also discussed.

cs.GT↗

arXiv2023

An Imitation Learning Based Algorithm Enabling Priori Knowledge Transfer in Modern Electricity Markets for Bayesian Nash Equilibrium Estimation

The Nash Equilibrium (NE) estimation in bidding games of electricity markets is the key concern of both generation companies (GENCOs) for bidding strategy optimization and the Independent System Operator (ISO) for market surveillance. However, existing methods for NE estimation in emerging modern electricity markets (FEM) are inaccurate and inefficient because the priori knowledge of bidding strategies before any environment changes, such as load demand variations, network congestion, and modifications of market design, is not fully utilized. In this paper, a Bayes-adaptive Markov Decision Process in FEM (BAMDP-FEM) is therefore developed to model the GENCOs' bidding strategy optimization considering the priori knowledge. A novel Multi-Agent Generative Adversarial Imitation Learning algorithm (MAGAIL-FEM) is then proposed to enable GENCOs to learn simultaneously from priori knowledge and interactions with changing environments. The obtained NE is a Bayesian Nash Equilibrium (BNE) with priori knowledge transferred from the previous environment. In the case study, the superiority of this proposed algorithm in terms of convergence speed compared with conventional methods is verified. It is concluded that the optimal bidding strategies in the obtained BNE can always lead to more profits than NE due to the effective learning from the priori knowledge. Also, BNE is more accurate and consistent with situations in real-world markets.

cs.GT↗

arXiv2021

Understanding User Topic Preferences across Multiple Social Networks

In recent years, social networks have shown diversity in function and applications. People begin to use multiple online social networks simultaneously for different demands. The ability to uncover a user's latent topic and social network preference is critical for community detection, recommendation, and personalized service across social networks. Unfortunately, most current works focus on the single network, necessitating new technology and models to address this issue. This paper proposes a user preference discovery model on multiple social networks. Firstly, the global and local topic concepts are defined, then a latent semantic topic discovery method is used to obtain global and local topic word distributions, along with user topic and social network preferences. After that, the topic distribution characteristics of different social networks are examined, as well as the reasons why users choose one network over another to create a post. Next, a Gibbs sampling algorithm is adopted to obtain the model parameters. In the experiment, we collect data from Twitter, Instagram, and Tumblr websites to build a dataset of multiple social networks. Finally, we compare our research to previous works, and both qualitative and quantitative evaluation results have demonstrated the effectiveness.

cs.SI↗

arXiv2019

Community Detection Across Multiple Social Networks based on Overlapping Users

With the rapid development of Internet technology, online social networks (OSNs) have got fast development and become increasingly popular. Meanwhile, the research works across multiple social networks attract more and more attention from researchers, and community detection is an important one across OSNs for online security problems, such as the user behavior analysis and abnormal community discovery. In this paper, a community detection method is proposed across multiple social networks based on overlapping users. First, the concept of overlapping users is defined, then an algorithm CMN NMF is designed to discover the stub communities from overlapping users based on the social relevance. After that, we extend each stub community in different social networks by adding the users with strong similarity, and in the end different communities are excavated out across networks. Experimental results show the advantage on effectiveness of our method over other methods under real data sets.

cs.SI↗

arXiv2017

Negative differential resistance and magnetoresistance in zigzag borophene nanoribbons

We investigate the transport properties of pristine zigzag-edged borophene nanoribbons (ZBNRs) of different widths, using the fist-principles calculations. We choose ZBNRs with widths of 5 and 6 as odd and even widths. The differences of the quantum transport properties are found, where even-N BNRs and odd-N BNRs have different current-voltage relationships. Moreover, the negative differential resistance (NDR) can be observed within certain bias range in 5-ZBNR, while 6-ZBNR behaves as metal whose current rises with the increase of the voltage. The spin filter effect of 36% can be revealed when the two electrodes have opposite magnetization direction. Furthermore, the magnetoresistance effect appears to be in even-N ZBNRs, and the maximum value can reach 70%.

cond-mat.mtrl-sci↗

arXiv2016

The magnetic and transport properties of edge passivated silicene nanoribbon by Mn atoms

The effect of chemical doping on the ZSiNRs with Mn as passivating element replacing H atoms at one edge are investigated by first principles calculations.The structures optimized in the typical ferromagnetic and antiferromagnetic coupling show that the system leads to an AFM state and achieve half metallic properties.Also,our first principle approach based on the Keldysh nonequilibrium Greens function method gives the spin dependent transport properties of the device. When the system changes from parallel to antiparallel configuration. the spin up current increases rapidly while the spin down current is still depressed. Further, it is found that the system is a quite good spin filtering device with nearly 80 percent spin filtering efficiency at a wide bias voltage region and therefore suitable for applications. The mechanisms for these phenomena are proposed in detail.

cond-mat.mtrl-sci↗