136,122 research outputs found
Energy Storage Sharing Strategy in Distribution Networks Using Bi-level Optimization Approach
In this paper, we address the energy storage management problem in
distribution networks from the perspective of an independent energy storage
manager (IESM) who aims to realize optimal energy storage sharing with
multi-objective optimization, i.e., optimizing the system peak loads and the
electricity purchase costs of the distribution company (DisCo) and its
customers. To achieve the goal of the IESM, an energy storage sharing strategy
is therefore proposed, which allows DisCo and customers to control the assigned
energy storage. The strategy is updated day by day according to the system
information change. The problem is formulated as a bi-level mathematical model
where the upper level model (ULM) seeks for optimal division of energy storage
among Disco and customers, and the lower level models (LLMs) represent the
minimizations of the electricity purchase costs of DisCo and customers.
Further, in order to enhance the computation efficiency, we transform the
bi-level model into a single-level mathematical program with equilibrium
constraints (MPEC) model and linearize it. Finally, we validate the
effectiveness of the strategy and complement our analysis through case studies
Next-generation optical access seamless Evolution: concluding results of the European FP7 project OASE
Increasing bandwidth demand drives the need for next-generation optical access (NGOA) networks that can meet future end-user service requirements. This paper gives an overview of NGOA solutions, the enabling optical access network technologies, architecture principles, and related economics and business models. NGOA requirements (including peak and sustainable data rate, reach, cost, node consolidation, and open access) are proposed, and the different solutions are compared against such requirements in different scenarios (in terms of population density and system migration). Unsurprisingly, it is found that different solutions are best suited for different scenarios. The conclusions drawn from such findings allow us to formulate recommendations in terms of technology, strategy, and policy. The paper is based on the main results of the European FP7 OASE Integrated Project that ran between January 1, 2010 and February 28, 2013
An Analysis of Next Generation Access Networks Deployment in rural areas
Next generation access networks (NGAN) will support a renewed electronic communication market where main opportunities lie in the provision of ubiquitous broadband connectivity, applications and content. From their deployment it is expected a wealth of innovations. Within this framework, the project reviews the variety of NGAN deployment options available for rural environments, derives a simple method for approximate cost calculations, and then discusses and compares the results obtained. Data for Spain are used for practical calculations, but the model is applicable with minor modifications to most of the rural areas of European countries. The final part of the paper is devoted to review the techno-economic implications of a network deployment in a rural environment as well as the adequacy and possible developments of the regulatory framework involve
Evolution of Supply Chain Collaboration: Implications for the Role of Knowledge
Increasingly, research across many disciplines has recognized the shortcomings of the traditional “integration prescription” for inter-organizational knowledge management. This research conducts several simulation experiments to study the effects of different rates of product change, different demand environments, and different economies of scale on the level of integration between firms at different levels in the supply chain. The underlying paradigm shifts from a static, steady state view to a dynamic, complex adaptive systems and knowledge-based view of supply chain networks. Several research propositions are presented that use the role of knowledge in the supply chain to provide predictive power for how supply chain collaborations or integration should evolve. Suggestions and implications are suggested for managerial and research purposes
On the interaction between Autonomous Mobility-on-Demand systems and the power network: models and coordination algorithms
We study the interaction between a fleet of electric, self-driving vehicles
servicing on-demand transportation requests (referred to as Autonomous
Mobility-on-Demand, or AMoD, system) and the electric power network. We propose
a model that captures the coupling between the two systems stemming from the
vehicles' charging requirements and captures time-varying customer demand and
power generation costs, road congestion, battery depreciation, and power
transmission and distribution constraints. We then leverage the model to
jointly optimize the operation of both systems. We devise an algorithmic
procedure to losslessly reduce the problem size by bundling customer requests,
allowing it to be efficiently solved by off-the-shelf linear programming
solvers. Next, we show that the socially optimal solution to the joint problem
can be enforced as a general equilibrium, and we provide a dual decomposition
algorithm that allows self-interested agents to compute the market clearing
prices without sharing private information. We assess the performance of the
mode by studying a hypothetical AMoD system in Dallas-Fort Worth and its impact
on the Texas power network. Lack of coordination between the AMoD system and
the power network can cause a 4.4% increase in the price of electricity in
Dallas-Fort Worth; conversely, coordination between the AMoD system and the
power network could reduce electricity expenditure compared to the case where
no cars are present (despite the increased demand for electricity) and yield
savings of up $147M/year. Finally, we provide a receding-horizon implementation
and assess its performance with agent-based simulations. Collectively, the
results of this paper provide a first-of-a-kind characterization of the
interaction between electric-powered AMoD systems and the power network, and
shed additional light on the economic and societal value of AMoD.Comment: Extended version of the paper presented at Robotics: Science and
Systems XIV, in prep. for journal submission. In V3, we add a proof that the
socially-optimal solution can be enforced as a general equilibrium, a
privacy-preserving distributed optimization algorithm, a description of the
receding-horizon implementation and additional numerical results, and proofs
of all theorem
On the interaction between Autonomous Mobility-on-Demand systems and the power network: models and coordination algorithms
We study the interaction between a fleet of electric, self-driving vehicles
servicing on-demand transportation requests (referred to as Autonomous
Mobility-on-Demand, or AMoD, system) and the electric power network. We propose
a model that captures the coupling between the two systems stemming from the
vehicles' charging requirements and captures time-varying customer demand and
power generation costs, road congestion, battery depreciation, and power
transmission and distribution constraints. We then leverage the model to
jointly optimize the operation of both systems. We devise an algorithmic
procedure to losslessly reduce the problem size by bundling customer requests,
allowing it to be efficiently solved by off-the-shelf linear programming
solvers. Next, we show that the socially optimal solution to the joint problem
can be enforced as a general equilibrium, and we provide a dual decomposition
algorithm that allows self-interested agents to compute the market clearing
prices without sharing private information. We assess the performance of the
mode by studying a hypothetical AMoD system in Dallas-Fort Worth and its impact
on the Texas power network. Lack of coordination between the AMoD system and
the power network can cause a 4.4% increase in the price of electricity in
Dallas-Fort Worth; conversely, coordination between the AMoD system and the
power network could reduce electricity expenditure compared to the case where
no cars are present (despite the increased demand for electricity) and yield
savings of up $147M/year. Finally, we provide a receding-horizon implementation
and assess its performance with agent-based simulations. Collectively, the
results of this paper provide a first-of-a-kind characterization of the
interaction between electric-powered AMoD systems and the power network, and
shed additional light on the economic and societal value of AMoD.Comment: Extended version of the paper presented at Robotics: Science and
Systems XIV and accepted by TCNS. In Version 4, the body of the paper is
largely rewritten for clarity and consistency, and new numerical simulations
are presented. All source code is available (MIT) at
https://dx.doi.org/10.5281/zenodo.324165
- …