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Analysis of Peer-to-Peer Energy Trading in a Dynamic Environment Using Stackelberg Game 动态环境下点对点能源交易的Stackelberg博弈分析
2021 Seventh Indian Control Conference (ICC) Pub Date : 2021-12-20 DOI: 10.1109/ICC54714.2021.9703159
Anchu Thomas, Mathew P. Abraham, Arya M G
{"title":"Analysis of Peer-to-Peer Energy Trading in a Dynamic Environment Using Stackelberg Game","authors":"Anchu Thomas, Mathew P. Abraham, Arya M G","doi":"10.1109/ICC54714.2021.9703159","DOIUrl":"https://doi.org/10.1109/ICC54714.2021.9703159","url":null,"abstract":"Peer-to-Peer (P2P) energy trading is the direct sharing of energy between grid-connected users. An efficient P2P energy trading platform paves way for sustainable development since it encourages more generation from renewables locally. Setting up a new P2P platform and bringing that to a successful one is challenging due to technical, economic and social factors. In this paper, we focus on the economic aspect of this challenge. We propose a novel model of P2P trading by which a policymaker can choose whether to encourage more local generation or consumption. We model the P2P energy trading as a single leader multiple follower Stackelberg game with the auctioneer or the policymaker as the single leader, and the prosumers (producer + consumer) as the followers. Depending on the bids submitted by the sellers (producers) and buyers (consumers), the auctioneer using double auction determines the winners for trading. For the winners, the auctioneer fixes a price maximizing the average social welfare of prosumers, and the prosumers decide their quantities maximizing their welfare functions. We show the existence and uniqueness of Stackelberg equilibrium in such a scenario. We also propose an algorithm to find the Stackelberg equilibrium price and quantities. We consider different test cases to analyze the existence of the Stackelberg equilibrium and the effect of the equilibrium on P2P energy trading.","PeriodicalId":382373,"journal":{"name":"2021 Seventh Indian Control Conference (ICC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116484363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Effects of the Steering and Repulsive Potential Interaction on UAV Swarm Formation Equilibrium 转向和斥力相互作用对无人机群编队平衡的影响
2021 Seventh Indian Control Conference (ICC) Pub Date : 2021-12-20 DOI: 10.1109/ICC54714.2021.9703153
A. Sharma, N. K. Sinha
{"title":"Effects of the Steering and Repulsive Potential Interaction on UAV Swarm Formation Equilibrium","authors":"A. Sharma, N. K. Sinha","doi":"10.1109/ICC54714.2021.9703153","DOIUrl":"https://doi.org/10.1109/ICC54714.2021.9703153","url":null,"abstract":"Unmanned Aerial Vehicle (UAV) swarms show great advantages in many applications such as surveillance, reconnaissance, and exploration because of the reduced computational expense, robustness, and less complexity. Artificial potential field (APF) is one such decentralized control strategy that steers UAV through steering and repulsive potential fields to achieve a formation. This paper extends to the previous research of the use of bifurcating APFs for swarm formation control by studying the unaccounted effects of repulsive potentials on the equilibrium states of the swarm system. Two prominent effects are observed, both of which are tied to the parameters of control law and the number of agents in the swarm. The radius of circular formation differs from estimates given by the nonlinear bifurcation analysis of steering potential and lower energy stable formation rings can be achieved with an increase in the number of agents. Finally, detailed simulation results validate the observed effects of the interaction between steering and repulsive potentials.","PeriodicalId":382373,"journal":{"name":"2021 Seventh Indian Control Conference (ICC)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125738710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Robust Reduced-Order Model Based Postprandial Glucose Regulation in Type-1 Diabetes: An IMC Approach 基于鲁棒降阶模型的1型糖尿病餐后血糖调节:一种IMC方法
2021 Seventh Indian Control Conference (ICC) Pub Date : 2021-12-20 DOI: 10.1109/ICC54714.2021.9703138
Krishma Prashar, Sahaj Saxena
{"title":"Robust Reduced-Order Model Based Postprandial Glucose Regulation in Type-1 Diabetes: An IMC Approach","authors":"Krishma Prashar, Sahaj Saxena","doi":"10.1109/ICC54714.2021.9703138","DOIUrl":"https://doi.org/10.1109/ICC54714.2021.9703138","url":null,"abstract":"The treatment of Type 1 diabetes using the artificial pancreas (AP) is considered one of the safety-critical and challenging control problems. The control law should be easily implemented with low complexity and good regulatory performances. In view of this, the present paper proposes a new model-based feedback regulation strategy in which the control-oriented insulin-glucose regulation system is simplified in such a way that the performance of the reduced-order model matches with the actual version. The dominant pole retention-like methodology is applied to obtain the reducedorder model. Now, based on the obtained reduced model, the internal model control (IMC) approach is employed to design a PI controller. Simulation studies have been conducted on the virtual patient and the applied approach produces satisfactory responses in presence of meal ingestion. The applied approach is also robust against model uncertainties.","PeriodicalId":382373,"journal":{"name":"2021 Seventh Indian Control Conference (ICC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127207095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
A Deep Unsupervised Learning Algorithm for Dynamic Data Clustering 动态数据聚类的深度无监督学习算法
2021 Seventh Indian Control Conference (ICC) Pub Date : 2021-12-20 DOI: 10.1109/ICC54714.2021.9703152
P. D. Pantula, S. Miriyala, K. Mitra
{"title":"A Deep Unsupervised Learning Algorithm for Dynamic Data Clustering","authors":"P. D. Pantula, S. Miriyala, K. Mitra","doi":"10.1109/ICC54714.2021.9703152","DOIUrl":"https://doi.org/10.1109/ICC54714.2021.9703152","url":null,"abstract":"Owing to the generation of vast amount of unlabelled dynamic data and the need to analyze them, deep unsupervised learning based clustering algorithms are gaining importance in the field of data science. Since the task of automated feature extraction is proficiently combined with the machine learning models in deep unsupervised learning algorithms, they are identified to be superior as compared to conventional dynamic similarity measure based clustering methods. In this context, the authors present a recurrent neural network (RNN) based clustering algorithm optimization, where the vital information representing the dynamic data (or time-series data) is extracted first and subsequently clustered using a soft clustering algorithm. This methodology not only ensures dynamic component extraction in terms of static features but also clusters them efficiently using an evolutionary clustering algorithm called Neuro-Fuzzy C-Means (NFCM) clustering, which reduces the large-scale optimization problem of FCM to small-scale along-with identification of optimal number of clusters. The proposed algorithm has been implemented on three different test data sets collected from machine learning repository and it was found that the results are 98-100% accurate.","PeriodicalId":382373,"journal":{"name":"2021 Seventh Indian Control Conference (ICC)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134049649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Cost-Optimal Control of Markov Decision Processes Under Signal Temporal Logic Constraints 信号时序逻辑约束下马尔可夫决策过程的成本最优控制
2021 Seventh Indian Control Conference (ICC) Pub Date : 2021-12-20 DOI: 10.1109/ICC54714.2021.9703164
K. C. Kalagarla, R. Jain, P. Nuzzo
{"title":"Cost-Optimal Control of Markov Decision Processes Under Signal Temporal Logic Constraints","authors":"K. C. Kalagarla, R. Jain, P. Nuzzo","doi":"10.1109/ICC54714.2021.9703164","DOIUrl":"https://doi.org/10.1109/ICC54714.2021.9703164","url":null,"abstract":"We present a method to find a cost-optimal policy for a given finite-horizon Markov decision process (MDP) with unknown transition probability, such that the probability of satisfying a given signal temporal logic specification is above a desired threshold. We propose an augmentation of the MDP state space to enable the expression of the STL objective as a reachability objective. In this augmented space, the optimal policy problem is re-formulated as a finite-horizon constrained Markov decision process (CMDP). We then develop a model-free reinforcement learning (RL) scheme to provide an approximately optimal policy for any general finite horizon CMDP problem. This scheme can make use of any off-the-shelf model-free RL algorithm and considers the general space of non-stationary randomized policies. Finally, we illustrate the applicability of our RL-based approach through two case studies.","PeriodicalId":382373,"journal":{"name":"2021 Seventh Indian Control Conference (ICC)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134207365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Comparison of Deep Reinforcement Learning Techniques with Gradient based approach in Cooperative Control of Wind Farm 深度强化学习与梯度法在风电场协同控制中的比较
2021 Seventh Indian Control Conference (ICC) Pub Date : 2021-12-20 DOI: 10.1109/ICC54714.2021.9703186
K. N. Pujari, Vivek Srivastava, S. Miriyala, K. Mitra
{"title":"Comparison of Deep Reinforcement Learning Techniques with Gradient based approach in Cooperative Control of Wind Farm","authors":"K. N. Pujari, Vivek Srivastava, S. Miriyala, K. Mitra","doi":"10.1109/ICC54714.2021.9703186","DOIUrl":"https://doi.org/10.1109/ICC54714.2021.9703186","url":null,"abstract":"The control settings of a turbines play a major role in increasing the energy production from a wind farm. The nonlinear interactions of wake between the turbines make optimal control of wind farm a challenging task. Therefore, it's hard to find the proper model based method to optimize the control settings. In the recent years, Reinforcement Learning (RL) has been emerging as a promising method for wind farm control. However, its efficacy is not evaluated when compared with nonlinear control strategies. In this study, yaw misalignment is used as control parameter to deflect the wakes and increase the power production from a 4×4 wind farm. A model-free Deep Deterministic Policy Gradient (DDPG) method and model-based iterative Linear Quadratic Regulator (iLQR) based Reinforcement Learning Techniques are utilized to optimize the yaw misalignments. To prove the efficiency of RL techniques, the results of DDPG and iLQR are compared with a nonlinear cooperative control strategy, Maximum Power Point Tracking solved through gradient based optimization approach.","PeriodicalId":382373,"journal":{"name":"2021 Seventh Indian Control Conference (ICC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115080361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Optimising a Real-Time Scheduler for Indian Railway Lines by Policy Search 基于政策搜索的印度铁路线实时调度优化
2021 Seventh Indian Control Conference (ICC) Pub Date : 2021-12-20 DOI: 10.1109/ICC54714.2021.9703176
Rohit Prasad, H. Khadilkar, Shivaram Kalyanakrishnan
{"title":"Optimising a Real-Time Scheduler for Indian Railway Lines by Policy Search","authors":"Rohit Prasad, H. Khadilkar, Shivaram Kalyanakrishnan","doi":"10.1109/ICC54714.2021.9703176","DOIUrl":"https://doi.org/10.1109/ICC54714.2021.9703176","url":null,"abstract":"The Indian railway network carries the largest number of passengers in the world, with over 8.4 billion transported in 2018, in addition to 1.2 billion tonnes of freight [1]. Nonetheless, the network has only about a tenth the “track-length per passenger” of the U.S., and half that of China [2]. This severe limitation of infrastructure, coupled with variability and heterogeneity in operations, raises significant challenges in scheduling. In this paper, we describe a policy search approach to decide arrival/departure times and track allocations for trains such that the resource and operating constraints of the railway line are satisfied, while the priority-weighted departure delay (PWDD) is minimised. We evaluate our approach on three large railway lines from the Indian network. We observe significant reductions of PWDD over traditional heuristics and a solution based on reinforcement learning.","PeriodicalId":382373,"journal":{"name":"2021 Seventh Indian Control Conference (ICC)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116016446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Trajectory Planning using Nonlinear Receding Horizon Optimization for an Autonomous Airship 基于非线性后退地平线优化的自主飞艇轨迹规划
2021 Seventh Indian Control Conference (ICC) Pub Date : 2021-12-20 DOI: 10.1109/ICC54714.2021.9703117
Sohan Suvarna, Hoam Chung, A. Sinha, R. Pant
{"title":"Trajectory Planning using Nonlinear Receding Horizon Optimization for an Autonomous Airship","authors":"Sohan Suvarna, Hoam Chung, A. Sinha, R. Pant","doi":"10.1109/ICC54714.2021.9703117","DOIUrl":"https://doi.org/10.1109/ICC54714.2021.9703117","url":null,"abstract":"This paper presents a control architecture for reference tracking of a small autonomous airship with input constraints. Nonlinear receding horizon optimization is used in order to generate a reference trajectory for the low-level controller to track. A simplified lateral dynamics model for an airship is also presented in this paper to be used for prediction. To investigate the efficacy of the proposed control algorithm, it is then implemented on a simulation platform and tested on a 6-degrees-of-freedom airship model to track a straight line and circular trajectory in the presence of wind disturbance. The simulation results indicate that the proposed planner generates a feasible trajectory for the low-level controller to track. A significant improvement in the tracking performance of the airship is also seen by the introduction of the planner.","PeriodicalId":382373,"journal":{"name":"2021 Seventh Indian Control Conference (ICC)","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128477914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Finite-Time Stability Analysis of a Distributed Microgrid Connected via Detail-Balanced Graph 详细平衡图连接分布式微电网的有限时间稳定性分析
2021 Seventh Indian Control Conference (ICC) Pub Date : 2021-12-20 DOI: 10.1109/ICC54714.2021.9703141
V. Vaishnav, Anoop Jain, Dushyant P. Sharma
{"title":"Finite-Time Stability Analysis of a Distributed Microgrid Connected via Detail-Balanced Graph","authors":"V. Vaishnav, Anoop Jain, Dushyant P. Sharma","doi":"10.1109/ICC54714.2021.9703141","DOIUrl":"https://doi.org/10.1109/ICC54714.2021.9703141","url":null,"abstract":"Distributed secondary control has been used as leading strategy to regulate the frequency and voltage of islanded AC microgrids, acting as cooperative multi-agent systems with droop controlled inverters. This paper considers the secondary control of an islanded microgrid as a leader-follower consensus problem, and implements a distributed finite-time strategy to restore the frequency of inverter-based generators, connected via detail-balanced directed topology. We show that the proposed controller restores the frequencies in the finite-time, while ensuring accurate real power-sharing among generators. We also provide an explicit expression for an upper bound on the convergence time. Simulations are given to illustrate the performance of the proposed controller. A comparison with undirected communication topology is also provided.","PeriodicalId":382373,"journal":{"name":"2021 Seventh Indian Control Conference (ICC)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114704300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Improving network's transition cohesion by approximating strongly damped waves using delayed self reinforcement 利用延迟自增强逼近强阻尼波，提高网络过渡内聚性
2021 Seventh Indian Control Conference (ICC) Pub Date : 2021-12-20 DOI: 10.1109/ICC54714.2021.9703122
Anuj Tiwari, Yoshua Gombo, S. Devasia
{"title":"Improving network's transition cohesion by approximating strongly damped waves using delayed self reinforcement","authors":"Anuj Tiwari, Yoshua Gombo, S. Devasia","doi":"10.1109/ICC54714.2021.9703122","DOIUrl":"https://doi.org/10.1109/ICC54714.2021.9703122","url":null,"abstract":"Cohesive networks aim to achieve similar response in each agent not only at steady state but also during transitions from one consensus value to another. Standard consensus-based approaches approximate the diffusion equation, which leads to decay of transition information for agents that are farther away from the leader, and results in loss of cohesion during rapid changes. Increasing the alignment strength in standard first-order consensus-based approaches enables each agent to respond faster to the changes in neighbor states. Nevertheless, it does not necessarily increase cohesion during the transition. Moreover, increasing the alignment strength also requires an increase in update bandwidth. In contrast, delayed self reinforcement (DSR) approach enables increased cohesion without increasing the update bandwidth. The main contribution of this article is to explain this increased cohesion with DSR by showing that the DSR approximates a strongly damped wave equation, where each agent not only attempts to align with its neighboring states but also to align with the rate of change of its neighboring states.","PeriodicalId":382373,"journal":{"name":"2021 Seventh Indian Control Conference (ICC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114871744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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