Resource management guidelines for the Norris Watershed : an analysis of harvest scheduling alternatives

The objectives of this study were to identify multiple-use management goals through a public participation program and to develop management guideline proposals for the Norris Watershed. The goals and guidelines were applied to an interrelated problem involving the scheduling of the decadal harvest of the area. An opportunity cost approach was used to consider conflicting objectives. Scheduling alternatives that were developed for harvesting the merchantable stands on the watershed included: 1. Scheduling to maximize present value. 2. Scheduling to provide an annual even flow of sawtimber volume and concomitantly maximize present value. 3. Scheduling to improve forage production capacity. 4. Scheduling to minimize visual impact of timber harvesting. 5. Scheduling to retain the most mast production capability. 6. Scheduling to minimize present value. 7. Price response scheduling. These alternatives were compared on the basis of present value of future gross receipts generated from timber sales, total gross receipts generated over the harvest period, and sawtimber volume removals. Analysis showed that in terms of present value the opportunity costs of implementing a particular scheduling option were less than expected. However, some scheduling options were found to be more desirable in terms of annual budget planning. The price response scheduling alternative was found to have potentially desirable effects by maximizing the present value and the total gross receipts from future sales, and by holding volume removals to a minimum estimated allowable cut

Short-term Self-Scheduling of Virtual Energy Hub Plant within Thermal Energy Market

Multicarrier energy systems create new challenges as well as opportunities in future energy systems. One of these challenges is the interaction among multiple energy systems and energy hubs in different energy markets. By the advent of the local thermal energy market in many countries, energy hubs' scheduling becomes more prominent. In this article, a new approach to energy hubs' scheduling is offered, called virtual energy hub (VEH). The proposed concept of the energy hub, which is named as the VEH in this article, is referred to as an architecture based on the energy hub concept beside the proposed self-scheduling approach. The VEH is operated based on the different energy carriers and facilities as well as maximizes its revenue by participating in the various local energy markets. The proposed VEH optimizes its revenue from participating in the electrical and thermal energy markets and by examining both local markets. Participation of a player in the energy markets by using the integrated point of view can be reached to a higher benefit and optimal operation of the facilities in comparison with independent energy systems. In a competitive energy market, a VEH optimizes its self-scheduling problem in order to maximize its benefit considering uncertainties related to renewable resources. To handle the problem under uncertainty, a nonprobabilistic information gap method is implemented in this study. The proposed model enables the VEH to pursue two different strategies concerning uncertainties, namely risk-averse strategy and risk-seeker strategy. For effective participation of the renewable-based VEH plant in the local energy market, a compressed air energy storage unit is used as a solution for the volatility of the wind power generation. Finally, the proposed model is applied to a test case, and the numerical results validate the proposed approach

Resource Allocation with Reverse Pricing for Communication Networks

Reverse pricing has been recognized as an effective tool to handle demand uncertainty in the travel industry (e.g., airlines and hotels). To investigate its viability for communication networks, we study the practical limitations of (operator-driven) time-dependent pricing that has been recently introduced, taking into account demand uncertainty. Compared to (operator-driven) time-dependent pricing, we show that the proposed pricing scheme can achieve "triple-win" solutions: an increase in the total average revenue of the operator; higher average resource utilization efficiency; and an increment in the total average payoff of the users. Our findings provide a new outlook on resource allocation, and design guidelines for adopting the reverse pricing scheme.Comment: to appear in IEEE International Conference on Communications (ICC) 2016, Kuala Lumpur, Malaysia (6 pages, 3 figures

Optimized Energy Management Strategy for Wind Plants with Storage in Energy and Reserve Markets

This paper addresses the joint operation of wind plants with energy storage systemsin multiple markets to increase the value of wind energy from an economic and technical point of view. The development of an optimized energy management allows scheduling the wind generation in energymarkets, as well as contributing to the system stability through the joint participation in frequency ancillary services. The market optimization maximizes market revenuesconsidering overallstoragecosts, while avoidingenergy imbalancesand market penalties. Moreover, wind power fluctuations, forecast errors and real-time reserverequirementsare controlledby the energy storagesystem and managed afterward through the participation in continuous intraday market. Furthermore, model predictive control approach enables a high compliance of reserve requirementsand a hugereduction of energy imbalancesin real-time operation. Different energy storagecapacities are selected in order to evaluate theircost-effectiveness enhancing the wind plant operation underthe considered study case.This work was partially supported by the Basque Government under Project Road2DC (ELKARTEK Research Program KK-2018/00083)

Health care operations management

Health care operations management has become a major topic for health care service providers and society. Operations research already has and further will make considerable contributions for the effective and efficient delivery of health care services. This special issue collects seven carefully selected papers dealing with optimization and decision analysis problems in the field of health care operations management

Decentralized Greedy-Based Algorithm for Smart Energy Management in Plug-in Electric Vehicle Energy Distribution Systems

Variations in electricity tariffs arising due to stochastic demand loads on the power grids have stimulated research in finding optimal charging/discharging scheduling solutions for electric vehicles (EVs). Most of the current EV scheduling solutions are either centralized, which suffer from low reliability and high complexity, while existing decentralized solutions do not facilitate the efficient scheduling of on-move EVs in large-scale networks considering a smart energy distribution system. Motivated by smart cities applications, we consider in this paper the optimal scheduling of EVs in a geographically large-scale smart energy distribution system where EVs have the flexibility of charging/discharging at spatially-deployed smart charging stations (CSs) operated by individual aggregators. In such a scenario, we define the social welfare maximization problem as the total profit of both supply and demand sides in the form of a mixed integer non-linear programming (MINLP) model. Due to the intractability, we then propose an online decentralized algorithm with low complexity which utilizes effective heuristics to forward each EV to the most profitable CS in a smart manner. Results of simulations on the IEEE 37 bus distribution network verify that the proposed algorithm improves the social welfare by about 30% on average with respect to an alternative scheduling strategy under the equal participation of EVs in charging and discharging operations. Considering the best-case performance where only EV profit maximization is concerned, our solution also achieves upto 20% improvement in flatting the final electricity load. Furthermore, the results reveal the existence of an optimal number of CSs and an optimal vehicle-to-grid penetration threshold for which the overall profit can be maximized. Our findings serve as guidelines for V2G system designers in smart city scenarios to plan a cost-effective strategy for large-scale EVs distributed energy management

A Stackelberg Game for Multi-Period Demand Response Management in the Smart Grid

This paper studies a multi-period demand response management problem in the smart grid where multiple utility companies compete among themselves. The user-utility interactions are modeled by a noncooperative game of a Stackelberg type where the interactions among the utility companies are captured through a Nash equilibrium. It is shown that this game has a unique Stackelberg equilibrium at which the utility companies set prices to maximize their revenues (within a Nash game) while the users respond accordingly to maximize their utilities subject to their budget constraints. Closed-form expressions are provided for the corresponding strategies of the users and the utility companies. It is shown that the multi- period scheme, compared with the single-period case, provides more incentives for the users to participate in the game. A necessary and sufficient condition on the minimum budget needed for a user to participate is provided.Comment: Accepted for Proc. 54th IEEE Conference on Decision and Contro