A cost-causal marginal participation method using min-max fairness for transmission services cost allocation

Fairness and system throughput, influenced by wireless interference, are major objectives of resource allocation in wireless networks. Whereas traditionally max-min fairness protocols have been developed for wired networks (where interference is not a factor for network performance), in this paper we investigate the problem of flow routing and fair bandwidth allocation under the constraints of the physical interference model for multihop wireless networks. A max-min fair (MMF) routing algorithm under physical interference constraints is proposed where multiple candidate paths coexist for each user to the base station. The algorithm is formulated as a multicommodity flow problem where the lexicographically largest bandwidth allocation vector is found among all optimal allocation vectors. We compare our approach with two MMF interference routing algorithms in the literature that use the protocol interference model to garner fair bandwidth allocation. We show that our algorithm performs better in terms of blocking probability, bandwidth usage and link load.

<p>This paper addresses the impact of renewable power generation such as photovoltaic and wind energy in the existing power system operations. Various modeling approaches and power quality/reliability analysis of these renewable energy sources in the electric power system by researchers and research organisations and utilities are outlined and their impacts are assessed. Challenges and protection schemes of renewable power integration into the existing grid are discussed through a detailed literature review and study of renewable integration into the Indian power system are outlined including potential planning and policy actions to support renewable energy integration in India.</p>

Mobile Crowdsensing (MCS) has become a new paradigm of collecting and merging a large number of sensory data by using rich sensor-equipped mobile terminals. Existing studies focusing on multi-task allocation with the objective of maximizing the social utility may result in the problem of unbalanced allocation due to the limited resources of workers, which may damage the social fairness, and requesters who suffer unfairness will choose to leave the system, thereby destroying the long-term stability of the system. To address this issue, we introduce max-min fairness into the design of a novel fairness-aware incentive mechanism for MCS. We first formalize the max-min fairness-aware multi-task allocation problem by using the sensing time threshold of tasks as a constraint. By modeling the max-min fairness-aware multi-task allocation problem as a Stackelberg game consisting of multi-leader and multi-follower, we next compute the unique Stackelberg equilibrium at which the utilities of both requesters and workers are maximized. Then, we design a greedy algorithm to achieve max-min fairness while meeting the sensing time threshold required by the task. Finally, simulation results further demonstrate the impact of intrinsic parameters on social utility and price of fairness, as well as the feasibility and effectiveness of our proposed max-min fairness-aware incentive mechanism.KeywordsMobile crowdsensingIncentive mechanismMulti-task allocationMax-Min fairnessStackelberg game

The problem of fair and feasible allocation of user throughputs in capacitated networks is investigated. The main contribution of the paper is an extension of network fairness, and in particular, max-min fairness from the traditional fixed- path model to a more versatile, routing-independent model. We show that the set of throughput configurations realizable in a capacitated network makes up a polyhedron, which gives rise to a max-min fair allocation completely analogous to the conventional case.

Considering the fairness between the users in physical-layer multigroup multicasting, the Max-Min Fairness (MMF) precoding problem is investigated under the per-antenna power constraints. A suboptimal precoding algorithm is proposed by decoupling the problem into a precoding direction vector design problem and a power allocation problem. Particularly, the SLNR (Signal to Leakage plus Noise Ratio) metric is considered in the precoding direction vector design to properly mitigate the inter-group interference. Moreover, aiming at the MMF criterion, the power allocation procedure can guarantee fairness between users. The simulation results show that the proposed algorithm has less complexity than the primary MMF algorithm at the cost of some performance loss, while has better performance than a low complex algorithm, and consequently provides a solution for the tradeoff between complexity and performance.

There is a tremendous growth in Indian power system since independence in 1947. An indicator of the growth is the Installed generating capacity which has increased from 1362 MW in December'1947 to - 1,15,000 MW by end of 2004. Indian power system has a hierarchical system. It consists of distribution Utilities i.e. State Electricity Boards (SEB)/Private companies, Central transmission utilities, Central Generation utility and Independent Power Producers; all combined together form the Regional Power System. Seeing the vast geographical stretch of India, there are five such regions. They are namely Northern Region, Southern Region, Eastern, Western Region and North Eastern Region. These Regional Grids have problems primarily due to insufficient installed generating and transmission capacity and is further aggravated because of inadequate reactive power compensation resulting in prolonged low/high voltage operations. High complexity of power system under resource deficit condition makes the interconnected Grid Operation a great challenge. POWERGRID, in the capacity of Central Transmission Utility (CTU) is entrusted the responsibility of Regional Grid Operation and has taken up this challenging task of maintaining the quality of bulk supply in the Region with inadequate margins in generation and transmission. POWERGRID has implemented the concept of Unified Load Despatch and Communication (ULDC) Schemes for reliable and integrated operation of regional grids. This scheme has already been implemented in Northern, Southern and North-Eastern Regional grids while it is under implementation in other Regions.

The max-min allocation problem has the following setting. There is a set A of m agents and a set I of n items. Each agent i 2 A has utility uij 2 R 0 for item j 2 I. Given a subset of items S I , the utility of this set to agent i is denoted as ui .S/ WD Pj2S uij . The max-min allocation problem is to find an allocation of items to agents such that the minimum utility among the agents is maximized. That is, mini2A ui .Si / is maximized, where Si I is the set of items allocated to agent i and Si \ Si 0 D ;. The problem naturally arises as an approach to maximize fairness. Fairness is an important concept arising in numerous settings ranging from border disputes in political science to frequency allocations in spectrum auctions. Max-min fairness is one of the standard notions of fairness and has been an object of study for decades [6]. Most of the older works, however, have focussed on divisible settings, that is, situations where the resource can be infinitely divided and allocated. Furthermore, the computational perspective, that is, how efficiently can one find a fair allocation, has not been a primary viewpoint. The max-min allocation problem is a combinatorial allocation problem where the items cannot be divided, and the interest is in designing polynomial time algorithms to obtain fair, or near-fair, allocations.

System Protection Schemes (SPS) are widely accepted as an effective tool for increasing the utilization of power networks by enhancing the resilience of the power system towards rare contingencies. These schemes are considered as 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> generation protection philosophy after the unit protection philosophy. In the last few years several SPS have been designed and implemented successfully in the Indian Power System. This paper discusses the need for SPS in Indian power system and provides a brief description of the major System Protection Schemes deployed in the Northern Regional Power System in India. The performance of these SPS post-commissioning has also been analyzed briefly. In the end the paper highlights the major issues associated with the deployment of SPS in the Indian context and also suggests the future scope of work in this area.

In this paper, we address the issue of recommending fairly from the aspect of providers, which has become increasingly essential in multistakeholder recommender systems. Existing studies on provider fairness usually focused on designing proportion fairness (PF) metrics that first consider systematic fairness. However, sociological researches show that to make the market more stable, max-min fairness (MMF) is a better metric. The main reason is that MMF aims to improve the utility of the worst ones preferentially, guiding the system to support the providers in weak market positions. When applying MMF to recommender systems, how to balance user preferences and provider fairness in an online recommendation scenario is still a challenging problem. In this paper, we proposed an online re-ranking model named Provider Max-min Fairness Re-ranking (P-MMF) to tackle the problem. Specifically, P-MMF formulates provider fair recommendation as a resource allocation problem, where the exposure slots are considered the resources to be allocated to providers and the max-min fairness is used as the regularizer during the process. We show that the problem can be further represented as a regularized online optimizing problem and solved efficiently in its dual space. During the online re-ranking phase, a momentum gradient descent method is designed to conduct the dynamic re-ranking. Theoretical analysis showed that the regret of P-MMF can be bounded. Experimental results on four public recommender datasets demonstrated that P-MMF can outperformed the state-of-the-art baselines. Experimental results also show that P-MMF can retain small computationally costs on a corpus with the large number of items.

It has become routine for large volumes of data to be generated, stored, and processed across geographically distributed datacenters. To run a single data analytic job on such geo-distributed data, recent research proposed to distribute its tasks across datacenters, considering both data locality and network bandwidth across datacenters. Yet, it remains an open problem in the more general case, where multiple analytic jobs need to fairly share the resources at these geo-distributed datacenters. In this paper, we focus on the problem of assigning tasks belonging to multiple jobs across datacenters, with the specific objective of achieving max-min fairness across jobs sharing these datacenters, in terms of their job completion times. We formulate this problem as a lexicographical minimization problem, which is challenging to solve in practice due to its inherent multi-objective and discrete nature. To address these challenges, we iteratively solve its single-objective subproblems, which can be transformed to equivalent linear programming (LP) problems to be efficiently solved, thanks to their favorable properties. As a highlight of this paper, we have designed and implemented our proposed solution as a fair job scheduler based on Apache Spark, a modern data processing framework. With extensive evaluations of our real-world implementation on Amazon EC2 and large-scale simulations, we have shown convincing evidence that max-min fairness has been achieved and the worst job completion time has been significantly improved using our new job scheduler.

This paper presents power system stabilizer tuning experience in the Indian power system. It explains the various regulatory provision and standards to be complied with for the PSS tuning. It compares the tuning procedures adopted by various utilities in India. It also shares a few case studies to highlight the challenges observed during PSS tuning and procedure overcoming these issues.

Power system planning is a capital intensive investment-decision problem. The majority of the conventional planning conducted since the last half a century has been based on the least cost approach, keeping in view the optimization of cost and reliability of power supply. Recently, renewable energy sources have found a niche in power system planning owing to concerns arising from fast depletion of fossil fuels, fuel price volatility as well as global climatic changes. Thus, power system planning is under-going a paradigm shift to incorporate such recent technologies. This paper assesses the impact of renewable sources using the portfolio theory to incorporate the effects of fuel price volatility as well as CO2 emissions. An optimization framework using a robust multi-objective evolutionary algorithm, namely NSGA-II, is developed to obtain Pareto optimal solutions. The performance of the proposed approach is assessed and illustrated using the Indian power system considering real-time design practices. The case study for Indian power system validates the efficacy of the proposed methodology as developing countries are also increasing the investment in green energy to increase awareness about clean energy technologies.

Optimal reactive power compensation has become an important issue to Indian power system due to initiatives taken towards national grid formation in the country. Presently new grid and southern grid are running in asynchronous mode. Central Transmission Utility (CTU) has forced the regional or state grids to maintain their reactive power compensation as per the requirement of grid as specified in Indian Electricity Grid Code (IEGC). In Indian power system most of the reactive power is injected at 33 KV and 11 KV levels with the help of high voltage (HV) shunt capacitor banks. This paper has proposed different methods to use the existing HV shunt capacitor banks installed at grid sub-stations (GSS) on 33 KV and 11 KV buses of Rajasthan Rajya Vidghyut Prasaran Nigam Ltd. (RRVPNL) power grid, a transmission network of Rajasthan state of India, on reduced capacity when there are no spare capacitor units available on GSS located in remote areas. The proposed different configurations of capacitor bank on reduced capacity are simulated in MATLAB/Simulink environment and also practically tested on the live system in the field.

Economic Assessment of Photovoltaic Energy Production Prospects in India

Wireless links are often unreliable and prone to transmission error, particularly when network users are mobile. These can degrade the performance in wireless networks, particularly for applications with tight quality-of-service requirements. A common remedy to this problem is channel coding. However, this per-link solution can compromise the link data rate, leading to an undesired end-to-end performance. In this paper, we show that this shortcoming can be mitigated if the end-to-end transmission rates and channel code rates are properly selected over multiple routing paths. We formulate a joint channel coding and end-to-end data rate allocation problem in multipath wireless networks with max-min fairness as the objective function. Our goal is to maximize the minimum throughput available among the network users. To cope with the fast and frequent changes in dynamic environments that are typical for vehicular networks, we address both adaptive and nonadaptive channel coding scenarios. Unlike similar formulations in single-path routing networks, in the multipath routing case, we face an optimization problem that is nonconvex and usually difficult to solve. We tackle the nonconvexity by using function approximation and iterative techniques from signomial programming. Simulation results confirm that by using channel coding jointly with multipath routing, we can significantly improve the end-to-end network performance compared with the case when only one of them is used in the network. Nonadaptive channel coding is also shown to achieve a high degree of optimality with much less complexity.