Penalty Price Research Articles

Equilibrium Problems With Equilibrium Constraints Analysis for Power Control and User Scheduling in NOMA Networks

With limited wireless resources but increasing demands for wireless applications, non-orthogonal multiple access (NOMA) has been considered as a promising technology for future wireless networks. Nevertheless, it remains challenging on motivating the mobile users (MU) to join NOMA networks, scheduling MUs on the same sub-bands, reducing interference among the MUs and setting optimal transmit power in the NOMA networks. In this paper, first we consider the quantization error due to the limitation of receivers’ equipments and the decoding error during the successive interference cancellation (SIC) procedure. Second, we model the resource allocation problems as the four-level equilibrium problems with equilibrium constraints (EPEC), where the wireless operator, on behalf of each MU, first predicts the utilities and assign the MU as a NOMA user (NMU) or a traditional MU (TMU). For the NMUs, the interference penalty pricing strategy of other NMUs are proposed. Based on the announced prices of all the NMUs, the sub-channel allocation is managed in either distributed or collaborative ways. According to the announced interference prices of other NMUs and the coalition results, the optimal transmit power to gain the maximum utility is considered. Through extensive simulation analysis, the superior performance obtained by using the proposed scheme is demonstrated.

Development of Revenue Model for Optimum Gas Distribution and Utilisation in Nigeria

The emergence of gas projects have increased the demand for natural gas. Producers of natural gas are faced with the challenge of gas distribution to users. At this point, in order to maintain demand and supply, a model is required to determine optimum distribution of gas. In this paper, models are developed for optimum gas distribution and utilisation in Nigeria. The models seek to evaluate the monetary returns accruable from the utilisation of gas resources produced in Nigeria for domestic uses. Many outlets were suggested for domestic utilisation of gas produced in Nigeria. These gas utilisation and monetization outlets were proved to have viability and competitive advantage when compared with the sales of the gas abroad. The domestic gas supply revenue formula was formulated by considering the quantity of gas each gas producer will produce, the demand of the gas locally, the price stipulated for the gas in order to ensure profitability when sold locally, the penalty price for companies willing to flare and not supply their gas, the gas deficiency price etc. The revenue formula helps the government and gas producers to determine the extent of financial returns accruable from the sale of gas for domestic utilisation. The Model is sufficient to help the government make necessary decisions and plans as regarding domestic gas aggregation in Nigeria.

Electricity Market Assessment in Wind Energy Integrated Power Systems with the Potential of Flexibility: A Boundary Condition Approach

This paper focuses on a dynamic equilibrium considering the flexible ramp market and demand response resources. With ever-swelling installation of variable renewable energies, demand response programs can play an important role in mitigating the system ramping deficiency. Hence in this paper, the ramping capability of demand response resources in procuring system ramp requirement is considered. The strategic behavior of different players is modeled through a multi-leader-common-follower game, in which suppliers and demand response aggregators are laid as the leaders and market operator is considered as the single follower of the game. In addition, a dynamic forward rolling process to find equilibria at the real-time market is proposed. The effect of considering demand response resources and flexible ramp penalty price on the strategic behavior of players in equilibrium is evaluated. Finally, the effectiveness of the proposed approach is verified on a three-firm system. While revealing demand response resources roles in mitigating ramping deficiency, the results show that how penalty price on flexible ramp violation can lead uplift payments to be formed.

Distributed Multi-Dimensional Pricing for Efficient Application Offloading in Mobile Cloud Computing

Offloading computation intensive applications to mobile cloud is promising for overcoming the problems of limited computational resources and energy of mobile devices. However, without considering the competition relationship of mobile users and cloudlets in the mobile cloud computing system, existing studies lack an incentive mechanism for the system to achieve efficient application offloading and cloud resource provisioning. In this paper, we design MPTMG, a Multi-dimensional Pricing mechanism based on Two-sided Market Game. We propose three types of prices: a multi-dimensional price corresponding to multi-dimensional resource allocation, a penalty price to encourage fair and high quality cloud services, and a benefit discount factor to motivate more even provisioning of resources on different dimensions in the cloud. Based on these prices, we propose a distributed price-adjustment algorithm for efficient resource allocation and QoS-aware offloading scheduling. We prove that the algorithm can converge in a finite number of iterations to the equilibrium core allocation at which the mobile cloud system achieves the Pareto efficiency by maximizing the total system benefit. To the best of our knowledge, this is the first paper that applies economic theories and pricing mechanisms to manage application offloading in mobile cloud systems. The simulation results demonstrate that our proposed pricing mechanism can significantly improve the system performance.

Multisources of Energy Contracting Strategy with
 an Ecofriendly Factor and Demand Uncertainties

This study presents a mathematical formulation to optimize contracting capacity strategiesof multisources of energy for institutional or industrial consumers considering demand uncertainties.The objective consists of minimizing the total costs composed of the different types of energy contractcapacity costs, penalty price, and an ecofriendly factor. The penalty price is charged on the demand ofenergy exceeding the total contract capacities. The ecofriendly factor encourages the use of renewableenergy and reduces the traditional energy used in the optimal mix of energy sources. The proposedmodel is tested based on demand of energy inspired from real data. These numerical experiments areanalyzed to illustrate the impact of encouraging the use of renewable energy sources by introducingthe ecofriendly factor and the influence of penalty price and uncertainty in the demand of energy.The results show that in the presence of low penalty price or low uncertainty a large amount ofecofriendly support is needed for using more renewable energy sources in the optimal contractcapacity combination.

Economically Effective and Intelligently Responsive Home Energy Management System

Energy management in home is one of the major issue now-a-days. There are different types of load like shiftable, non-shiftable, seasonal loads and auxiliary loads. In this research article, an energy management system is proposed for home which helps to schedule different loads on the basis of their types and price. It will help to minimize the cost of electricity by shifting load from peak time to off peak time. Emission will be minimized by charging penalty by adopting multi-objective optimization. Each source of energy has its own price of penalty with respect to time. Penalty is charged to minimize the use of sources like commercial supply and diesel generators which emits hazardous gases. In proposed model, user will get electricity from commercial supply, diesel generators and solar panels to provide continuous supply of electricity to fulfill the energy demand. The shiftable loads will be shifted from peak time to off peak time and higher price source to lower price source to minimize the overall price. In this research, we have proposed an EEIR (Economically Effective and Intelligently Responsive) HEMS (Home Energy Management System) by solving multi-objective optimization problem from BILP (Binary Integer Linear Programming) using branch and bound algorithm.

Comparison Between Different Penalty Price Models for Determination of Optimal Total Transfer Capability in the Presence of Wind Farms

Total transfer capability (TTC) is an important index which presents the maximum transfer ability between the generation and consumer’s region while satisfying the system limitation. Moreover, by applying renewable energy resources such as wind farms, with high penetration, managing and evaluating the system has become an important issue and, consequently, brought new challenges in the power market. In this paper, a method is proposed to evaluate the optimal TTC in the presence of wind farms. In order to simulate the system state, sequential Monte Carlo is used. Optimal power flow along with two models for penalty cost is used as decision-making method in order to establish the optimum TTC level. Reliability test system from IEEE is considered to present the effectiveness of the proposed method.

Game-Theoretic Optimization for Machine-Type Communications Under QoS Guarantee

Massive machine-type communication (mMTC) is a new focus of services in fifth generation communication networks. The associated stringent delay requirement of end-to-end (E2E) service deliveries poses technical challenges. In this paper, we propose a joint random access and data transmission protocol for mMTC to guarantee E2E service quality of different traffic types. First, we develop a priority-queueing-based access class barring (ACB) model and a novel effective capacity is derived. Then, we model the priority-queueing-based ACB policy as a noncooperative game, where utility is defined as the difference between effective capacity and access penalty price. We prove the existence and uniqueness of Nash equilibrium (NE) of the noncooperative game, which is also a submodular utility maximization problem and can be solved by a greedy updating algorithm with convergence to the unique NE. To further improve the efficiency, we present a price-update algorithm, which converges to a local optimum. Simulations demonstrate the performance of the derived effective capacity and the effectiveness of the proposed algorithms.

An Energy Imbalance Settlement Mechanism Considering Decision-Making Strategy of Retailers Under Renewable Portfolio Standard

Before the establishment of the spot electricity market in China, an energy imbalance settlement (EIS) mechanism has been proposed as a transitional solution to address the imbalances between actual consumption and contracted energy in the forward electricity market. It plays a crucial role in cultivating electricity retailers, maintaining the stability of the balancing account, and promoting a smooth transition to market-oriented reforms. Given this background, a novel EIS mechanism with a piecewise linear penalty pricing scheme is proposed, learning from the performance-based regulation (PBR) in distribution systems. For optimizing the parameters in the proposed EIS mechanism, a stochastic bilevel model is presented considering two kinds of stakeholders involved, namely the power exchange (PX) and retailers. In the upper-level model, an optimal parameter setting model for policymakers to minimize the variance of deposit in the balancing account of PX is presented. In the lower-level model, a decision-making tool for retailers under the renewable portfolio standard (RPS) is developed. As a self-balance measure of retailers, flexible demands are incorporated into the lower-level problem. Consumer psychology is applied to quantify customer consumption adjustments in response to the financial incentives given by retailers. The risk faced by the retailers is modeled using conditional value-at-risk (CVaR), taking into account the uncertainties associated with renewable energy production and customer demand. Simulation results of a provincial electricity market in China show that the proposed method can effectively motivate retailers to improve their imbalance management capability and assist policymakers in determining the parameters of the EIS mechanism. Besides, the proposed method provides insights into the impacts of parameter setting of the EIS mechanism on the behavior and performance of retailers.

Receiver-Initiated Handshaking MAC Based on Traffic Estimation for Underwater Sensor Networks ‡.

In underwater sensor networks (UWSNs), the unique characteristics of acoustic channels have posed great challenges for the design of medium access control (MAC) protocols. The long propagation delay problem has been widely explored in recent literature. However, the long preamble problem with acoustic modems revealed in real experiments brings new challenges to underwater MAC design. The overhead of control messages in handshaking-based protocols becomes significant due to the long preamble in underwater acoustic modems. To address this problem, we advocate the receiver-initiated handshaking method with parallel reservation to improve the handshaking efficiency. Despite some existing works along this direction, the data polling problem is still an open issue. Without knowing the status of senders, the receiver faces two challenges for efficient data polling: when to poll data from the sender and how much data to request. In this paper, we propose a traffic estimation-based receiver-initiated MAC (TERI-MAC) to solve this problem with an adaptive approach. Data polling in TERI-MAC depends on an online approximation of traffic distribution. It estimates the energy efficiency and network latency and starts the data request only when the preferred performance can be achieved. TERI-MAC can achieve a stable energy efficiency with arbitrary network traffic patterns. For traffic estimation, we employ a resampling technique to keep a small computation and memory overhead. The performance of TERI-MAC in terms of energy efficiency, channel utilization, and communication latency is verified in simulations. Our results show that, compared with existing receiver-initiated-based underwater MAC protocols, TERI-MAC can achieve higher energy efficiency at the price of a delay penalty. This confirms the strength of TERI-MAC for delay-tolerant applications.

Energy Market Design for Renewable Resources: Imbalance Settlements and Efficiency–Robustness Tradeoffs

This paper examines optimal energy market bidding behavior for stochastic resources when energy imbalance payments are determined endogenously from market clearing conditions. Some electricity markets require wind producers to settle day-ahead offers based on their real-time energy production. This is usually justified by economic and cost-causation principles, without considering the effects of these policies on curtailment and system balancing costs. Under these real-time settlement requirements, demand and wind forecast errors impact the effective penalty price, so strategic behavior affects the penalty price paid by producers. This paper formulates a two-stage game where producers place energy bids in the day-ahead market and then choose a curtailment level in real-time, and it derives the symmetric subgame perfect equilibrium strategy for producers. It details how supply curve convexity and resource correlation can lead wind producers to bid conservatively even when they are not risk averse. The results show that market-based pricing mechanisms improve tradeoffs between system efficiency and mean squared bid error as compared to the case where penalty levels are determined by alternative policies, suggesting additional benefits of market-based penalty prices beyond those previously studied.

The price of flexibility - a case study on septanoses as pyranose mimetics.

Seven-membered ring mimetics of mannose were studied as ligands for the mannose-specific bacterial lectin FimH, which plays an essential role in the first step of urinary tract infections (UTI). A competitive binding assay and isothermal titration calorimetry (ITC) experiments indicated an approximately ten-fold lower affinity for the seven-membered ring mannose mimetic 2-O-n-heptyl-1,6-anhydro-d-glycero-d-galactitol (7) compared to n-heptyl α-d-mannopyranoside (2), resulting exclusively from a loss of conformational entropy. Investigations by solution NMR, X-ray crystallography, and molecular modeling revealed that 7 establishes a superimposable H-bond network compared to mannoside 2, but at the price of a high entropic penalty due to the loss of its pronounced conformational flexibility. These results underscore the importance of having access to the complete thermodynamic profile of a molecular interaction to "rescue" ligands from entropic penalties with an otherwise perfect fit to the protein binding site.

Offline penalty price determination method for transmission thermal constraint relaxations

This paper proposes an offline penalty price determination process for transmission thermal constraint relaxations. System operators utilize various market models, which are highly complex due to operating requirements as well as physical restrictions of assets, to manage electric energy markets while ensuring a reliable supply of electric power. System operators enable constraint relaxations in market models by allowing certain constraints to be relaxed for penalty prices. Constraint relaxation practices help system operators to cope with model infeasibility, obtain possible gains in market surplus, and cap shadow prices. A proper selection of penalty prices is imperative due to the influence that penalty prices have on generation scheduling and market settlement; however, current industry practices do not consider the true cost of the relaxations. This work introduces a systematic methodology to capture the cost of relaxations considering probabilistic weather conditions and associated conductor degradation risk. The numerical analysis evaluates the effectiveness of the proposed method on an electric energy market; the results show that exercising transmission thermal constraint relaxations with a proper selection of penalty prices can provide net benefits to market participants.

Coordinating a trust-embedded two-tier supply chain by options with multiple transaction periods

Trust widely works in supply chain practices and deeply affects supply chain decisions. Full trust in transactions and inaccurate demand forecasts are most likely to lead to biased decisions and low supply chain performances. Therefore, we propose a trust updating model to quantify decision-maker’s trust. The model presents the variation of trust over time and is helpful to evaluate decision-maker’s trust level in each transaction. In the model, a coordination mechanism with contract is designed to mitigate each supply chain partner’s ordering risk. Optimal order strategies of both manufactures and retailers in a supply chain under a bidirectional option contract are analysed in this paper. We find the manufacturer’s optimal production quantity is positively affected by penalty price in the contract and its bounds are given by simulation analysis. Our findings in this paper indicate that the proposed coordination mechanism with bidirectional option contract is profitable to all supply chain partners in long-term transactions. Meanwhile, the coordination mechanism helps the supply chain partners to catch up with marketing fluctuations and enhances the supply chain trust and partnerships. Finally, some simulation experiments are employed to obtain more observations.

A Multi-Leader Multi-Follower Stackelberg Game for Resource Management in LTE Unlicensed

It is known that the capacity of the cellular network can be significantly improved when cellular operators are allowed to access the unlicensed spectrum. Nevertheless, when multiple operators serve their user equipments (UEs) in the same unlicensed spectrum, the inter-operator interference management becomes a challenging task. In this paper, we develop a multi-operator multi-UE Stackelberg game to analyze the interaction between multiple operators and the UEs subscribed to the services of the operators in unlicensed spectrum. In this game, to avoid intolerable interference to the Wi-Fi access point (WAP), each operator sets an interference penalty price for each UE that causes interference to the WAP, and the UEs can choose their sub-bands and determine the optimal transmit power in the chosen sub-bands of the unlicensed spectrum. Accordingly, the operators can predict the possible actions of the UEs and hence set the optimal prices to maximize its revenue earned from UEs. Furthermore, we consider two possible scenarios for the interaction of operators in the unlicensed spectrum. In the first scenario, referred to as the non-cooperative scenario, the operators cannot coordinate with each other in the unlicensed spectrum. A sub-gradient approach is applied for each operator to decide its best-response action based on the possible behaviors of others. In the second scenario, referred to as the cooperative scenario, all operators can coordinate with each other to serve UEs and control the UEs' interference in the unlicensed spectrum. Simulation results have been presented to verify the performance improvement that can be achieved by our proposed schemes.

Interference pricing in 5G ultra‐dense small cell networks: a Stackelberg game approach

Being one of core characteristics for 5G cellular networks, ultra-dense small cell network is an effective approach to reuse the spectrum and achieve high data rate transmission in the wireless communication networks. However, because of sharing the spectrum resources, the interference problem among the macrocell base stations (MBS) and the small cell base stations (SCBSs) is hard to address. In this study, the authors model the scenario as a Stackelberg game, where the MBS act as the leader and all SCBSs act as followers. In the game, the MBS set its interference penalty price first, based on the prices the MBS then determines its channel allocation schemes to all SCBSs. Observing the interference penalty price and the amount of allocated channels performed by the MBS, each SCBS then determines its transmit power to achieve its optimal utility. Because of the first-move advantage, the MBS is able to predict the reactions of each SCBS and make optimal strategies. Simulation results show the correctness of the analysis and the significant benefits when the power control and channel allocation are jointly considered in the proposed schemes.

Analysis of reserve relaxations in electric energy markets

Electric power grids are some of the most complex engineering systems today. System operators must manage generation fleets with complex operating requirements and transmission assets spanning thousands of miles, while maintaining synchronism and meeting strict reliability criteria. Given the limited computational capabilities of modern computers, the Independent System Operators (ISOs) and Regional Transmission Organizations (RTOs) cannot fully integrate all of these complexities in their market model. Thus to ensure scalability, ISOs and RTOs employ a number of approximations within their market models, including proxy reserve requirements that attempt to achieve, but does not guarantee, N−1 reliability. Since the market model is an approximate model itself, and the solution needs to be corrected nevertheless, market management tools allow select constraints to be relaxed for a set penalty price. This paper examines the impacts of different penalty price schemes, for relaxation of reserve requirements, on system N−1 reliability and market outcomes. In particular, the post contingency network violations for the relaxed and non-relaxed market solutions are compared. Moreover, the final cost after making necessary corrections to arrive at the N−1 secure solutions are presented and compared for the relaxed and non-relaxed cases. The paper demonstrates that reserve relaxation does not significantly increase post contingency violations for the market solution or the total system cost after the adjustment phase. Thus, if performed for appropriate reasons such as controlling price or achieving feasibility, reserve relaxation is a justifiable practice in power system operations.

Impacts of Constraint Relaxations on Power System Operational Security

Constraint relaxations by definition mean that certain constraints are allowed to be violated in energy market models for a pre-determined penalty price. System operators utilize this mechanism in an effort to impose a price-cap on shadow prices in the markets. In addition, constraint relaxations can serve as corrective approximations that help to reduce the occurrence of infeasible or extreme solutions in market models. This paper aims to capture the impact constraint relaxations have on AC operating conditions. Moreover, this analysis also provides a better understanding of the correlation between DC market models and AC systems, and analyzes how relaxations in market models propagate to system operations. This information can be used to assess the risk associated with constraint relaxations. The practice of constraint relaxations was replicated in this paper using a test case and a real-life large-scale system, while capturing both the energy market aspects and AC system performance. System performance investigation included static and dynamic security analyses for base-case and post-contingency conditions.

Designing an emissions trading scheme for China with a dynamic computable general equilibrium model

To fulfill its Copenhagen pledges to control carbon emissions and mitigate climate change, China plans to establish a nationwide emissions trading scheme (ETS) in 2016. This paper develops a multi-sector dynamic computable general equilibrium model with an ETS module to study the appropriate ETS policy design, including a carbon cap, permit allocation and supplementary policies (e.g., penalty policies and subsidy policies). The main results are as follows. (1) To achieve China's Copenhagen pledge, the equilibrium nationwide carbon price is observed to be between 36 and 40 RMB yuan per metric ton. (2) The ETS policy has a cost-effective mitigation effect by improving China's production and energy structures with relatively little economic harm. (3) Various ETS sub-policies should be carefully designed to balance economic growth and carbon mitigation. In particular, the carbon cap should be set according to China's Copenhagen pledge. A relatively large distribution ratio of free permits, the output-based grandfathering rule for free permits, a penalty price (on illegitimate emissions) slightly above the carbon price, and a sufficient subsidy (from ETS revenue) are strongly recommended in the early stages to avoid significant economic loss. These designs can be adjusted in later stages to enhance the mitigation effect.

Sensing Transmission Tradeoff Over Penalty for Miss Detection in Cognitive Radio Network

In this paper, a challenge issue `sensing and data transmission trade-off' is addressed. Different from existing efforts, penalty is taken as a rule of regulation for spectrum sensing and access in order to possibly protect primary's activity. Specifically, secondary user needs to pay rental fee to primary user for opportunistic access. However, penalty will be charged from secondary user as well if it has transmitted on the miss estimated spectrum, in which, an negative secondary raw utility will be generated when the penalty value is properly decided. Thus the scheme will further force secondary user to improve its sensing accuracy and select the best transmission strategy when compared with conventional sensing access model. Based on the scheme, in this research, the bounds for proper rental and penalty prices are derived. Further, within both proper prices region, an optimization problem for setting sensing duration and transmission power is formulated. Through investigation, convexity of the problem is hard to be obtained at global view. But thanks to the special property that the problem is local convex when fixing any one of targeted variables, a Lagrange method based iterative algorithm is presented to find solution within a decent convergence of no more than 10 iterations.