A Bi-Level Model for a Closed-Loop Agricultural Supply Chain Considering Biogas and Compost

In today's competitive marketplace, to increase customer satisfaction and profitability, supply chain management has become more prominent. Therefore, thorough planning and designing the supply chain by seeing all levels and units are essential to growing the efficiency of the entire supply chain. In the present study, an eight-echelon network is designed for a closed-loop agricultural supply chain. These eight echelons are consist of suppliers, farms, distribution centers (DCs), customers, recycling depots, biogas centers, compost production centers, and biogas applicants. To design the agricultural logistics network, a bi-level programming mathematical model is presented that optimizes the network costs and profits. Also, some meta-heuristics and hybrid meta-heuristics are applied for solving the formulated problem. It should be noted that bi-level programming problems are part of the NP-hard class and due to the computational complexity of the problems, the meta-heuristic algorithms are utilized. Finally, various comparisons and analyses are performed to evaluate the model's performance and the capabilities of the solution methods.

Techno-economic comparison of technology options for deep decarbonization and electrification of residential heating.

This study aims to provide detailed information on the key technologies that utilize renewables for decarbonization and electrification of the residential heating sector. To contextualize and compare the economics of the technologies, a levelized cost model is employed to perform a comparative analysis for a dense urban area in Switzerland. The outcome shows that decarbonization of the heat supply with a dominant share of renewables is feasible, but it is challenged by the high cost of some options. In the given context (current energy and CO2 prices, no coercive measures), the rapid shift from conventional boilers to electrification via decentralized heat pumps and/or the introduction of targeted small-scale thermal energy networks utilizing cheap local resources like industrial excess heat is the most viable option. The replacement of natural gas boilers with electrification technologies also is recommendable because it would result in a sixfold reduction in specific CO2 emissions. Wide-scale application of heat pumps may require significant electricity grid reinforcement which ultimately may escalate the costs. Large-scale district heating systems are currently relatively expensive due to the high network costs and require a sustainable financing mechanism. To speed up the energy transition, policy interventions by the government are urgently needed.

Deep Reinforcement Learning-Based Network Routing Technology for Data Recovery in Exa-Scale Cloud Distributed Clustering Systems

Research has been conducted to efficiently transfer blocks and reduce network costs when decoding and recovering data from an erasure coding-based distributed file system. Technologies using software-defined network (SDN) controllers can collect and more efficiently manage network data. However, the bandwidth depends dynamically on the number of data transmitted on the network, and the data transfer time is inefficient owing to the longer latency of existing routing paths when nodes and switches fail. We propose deep Q-network erasure coding (DQN-EC) to solve routing problems by converging erasure coding with DQN to learn dynamically changing network elements. Using the SDN controller, DQN-EC collects the status, number, and block size of nodes possessing stored blocks during erasure coding. The fat-tree network topology used for experimental evaluation collects elements of typical network packets, the bandwidth of the nodes and switches, and other information. The data collected undergo deep reinforcement learning to avoid node and switch failures and provide optimized routing paths by selecting switches that efficiently conduct block transfers. DQN-EC achieves a 2.5-times-faster block transmission time and 0.4-times-higher network throughput than open shortest path first (OSPF) routing algorithms. The bottleneck bandwidth and transmission link cost can be reduced, improving the recovery time approximately twofold.

Recent Development in the Design of Wind Deflectors for Vertical Axis Wind Turbine: A Review

Developments in the design of wind turbines with augmentation are advancing around the globe with the goal of generating electricity close to the user in built-up areas. This is certain to help lessen the power generation load as well as distribution and transmission network costs by reducing the distance between the user and the power source. The main objectives driving the development and advancement of vertical-axis wind turbines are increasing the power coefficient and the torque coefficient by optimizing the upstream wind striking on the rotor blades. Unlike horizontal-axis wind turbines, vertical axis turbines generate not only positive torque but also negative torque during operation. The negative torque generated by the returning blade is a key issue for vertical-axis wind turbines (VAWTs) that is counterproductive. Installation of wind deflectors for flow augmentation helps to reduce the negative torque generated by the returning blades as well as enhance the positive torque by creating a diversion in the upstream wind towards the forwarding blade during operation. This paper reviews various designs, experiments, and CFD simulations of wind deflectors reported to date. Optimization techniques for VAWTs incorporating wind deflectors are discussed in detail. The main focus of the review was on the installation position and orientation of the deflectors and their potential contribution to increasing the power coefficient. Topics for future study are suggested in the conclusion section of the paper.

Optimization-driven framework to understand health care network costs and resource allocation

In the last several decades, the U.S. Health care industry has undergone a massive consolidation process that has resulted in the formation of large delivery networks. However, the integration of these networks into a unified operational system faces several challenges. Strategic problems, such as ensuring access, allocating resources and capacity efficiently, and defining case-mix in a multi-site network, require the correct modeling of network costs, network trade-offs, and operational constraints. Unfortunately, traditional practices related to cost accounting, specifically the allocation of overhead and labor cost to activities as a way to account for the consumption of resources, are not suitable for addressing these challenges; they confound resource allocation and network building capacity decisions. We develop a general methodological optimization-driven framework based on linear programming that allows us to better understand network costs and provide strategic solutions to the aforementioned problems. We work in collaboration with a network of hospitals to demonstrate our framework applicability and important insights derived from it.

Comparison of Economical and Technical Photovoltaic Hosting Capacity Limits in Distribution Networks

Power distribution networks are transitioning from passive towards active networks considering the incorporation of distributed generation. Traditional energy networks require possible system upgrades due to the exponential growth of non-conventional energy resources. Thus, the cost concerns of the electric utilities regarding financial models of renewable energy sources (RES) call for the cost and benefit analysis of the networks prone to unprecedented RES integration. This paper provides an evaluation of photovoltaic (PV) hosting capacity (HC) subject to economical constraint by a probabilistic analysis based on Monte Carlo (MC) simulations to consider the stochastic nature of loads. The losses carry significance in terms of cost parameters, and this article focuses on HC investigation in terms of losses and their associated cost. The network losses followed a U-shaped trajectory with increasing PV penetration in the distribution network. In the investigated case networks, increased PV penetration reduced network costs up to around 40%, defined as a ratio to the feeding secondary transformer rating. Above 40%, the losses started to increase again and at 76–87% level, the network costs were the same as in the base cases of no PVs. This point was defined as the economical PV HC of the network. In the case of networks, this level of PV penetration did not yet lead to violations of network technical limits.

Design and Analysis of a Symmetric Log Star Graph with a Smaller Network Cost Than Star Graphs

Graphs are used as models to solve problems in fields such as mathematics, computer science, physics, and chemistry. In particular, torus, hypercube, and star graphs are popular when modeling the connection structure of processors in parallel computing because they are symmetric and have a low network cost. Whereas a hypercube has a substantially smaller diameter than a torus, star graphs have been presented as an alternative to hypercubes because of their lower network cost. We propose a novel log star (LS) that is symmetric and has a lower network cost than a star graph. The LS is an undirected, recursive, and regular graph. In LSn, the number of nodes is n! while the degree is 2log2n − 1 and the diameter is 0.5n(log2n)2 + 0.75nlog2n. In this study, we analyze the basic topological properties of LS. We prove that LSn is a symmetrical connected graph and analyzed its subgraph characteristics. Then, we propose a routing algorithm and derive the diameter and network cost. Finally, the network costs of the LS and star graph-like networks are compared.

Electricity Tariff Design in the Context of an Ambitious Green Transition

Current tariff designs do not incentivize efficient or equitable responses by active customers adopting renewable self-generation or providing flexibility in a future fully decarbonized electricity system. This chapter revises current practices in Europe and, based on the revisited principles of efficiency and equity, proposes a first benchmark for tariff design. Forward-looking peak-coincident network charges that reflect network incremental costs and fixed charges that collect residual network costs and policy costs are recommended. No one-size-fits-all model exists, in practice. These are guidelines for regulators when dealing with the trade-offs between the tariff legacy and the new requirements imposed by this energy transition.

Reasoning on the Efficiency of Distributed Complex Event Processing

Complex event processing (CEP) evaluates queries over streams of event data to detect situations of interest. If the event data are produced by geographically distributed sources, CEP may exploit in-network processing that distributes the evaluation of a query among the nodes of a network. To this end, a query is modularized and individual query operators are assigned to nodes, especially those that act as data sources. Existing solutions for such operator placement, however, are limited in that they assume all query results to be gathered at one designated node, commonly referred to as a sink. Hence, existing techniques postulate a hierarchical structure of the network that generates and processes the event data. This largely neglects the optimisation potential that stems from truly decentralised query evaluation with potentially many sinks. To address this gap, in this paper, we propose Multi-Sink Evaluation (MuSE) graphs as a formal computational model to evaluate common CEP queries in a decentralised manner. We further prove the completeness of query evaluation under this model. Striving for distributed CEP that can scale to large volumes of high-frequency event streams, we show how to reason on the network costs induced by distributed query evaluation and prune inefficient query execution plans. As such, our work lays the foundation for distributed CEP that is both, sound and efficient.

Relationship between mains costs and voltage switches

The article deals with the issues of interconnection of network costs and power quality, in particular voltage deviations. The graphs of the distribution of voltage deviations for electrical receivers are given for two calculated load modes - the highest (100%) and the lowest (25%), corresponding to the choice of wires of 10 and 0.38 kV overhead lines for economic loads and voltage stabilization on 10 kV buses of 35/10 kV RTP at the level of + 5%.