This paper presents a new method for assessing the amount of photovoltaics that can be accommodated on a distribution feeder before disrupting the normal operational conditions, commonly referred to hosting capacity. An optimal-capacity-based (OCB) PV allocation method is proposed to evaluate PV hosting capacity. We fist use load allocation method to allocate realistic load profiles to each load node down to each household. Instead of randomly assigning the installed capacity of PV to each household, the optimal size of PV for each house is first calculated based on the annual load profiles. Different PV allocation methods for hosting capacity calculation are first compared using the IEEE 123-bus system as a benchmark. An actual distribution feeder in North Carolina area is used to validate the results in realistic distribution systems. The simulation results show that the impact of PV capacity allocation methods on hosting capacity assessment is significant. We also investigate the zonal allocation method for weak zone identification to address the cluster phenomena in technology diffusion.
The problems of determining the optimal power allocation, within maximum power bounds, to (i) maximize the minimum Shannon capacity, and (ii) minimize the weighted latency are considered. In the first case, the global optima can be achieved in polynomial time by solving a sequence of linear programs (LP). In the second case, the original non-convex problem is replaced by a convex surrogate (a geometric program), using a functional approximation. Since the approximation error is relatively low, the optima of the surrogate is close to the global optimal point of the original problem. In either c…
Uncertainty-Aware Grid Planning in the Real World: A Method Enabling Large-Scale, Two-Stage Adaptive Robust Optimization for Capacity Expansion Planning
Princeton University（普林斯顿大学）；RFF-CMCC European Institute on Economics and the Environment（RFF-CMCC欧洲经济与环境研究所）；Binghamton University（宾厄姆顿大学）
University of Shanghai for Science and Technology（上海理工大学）；Virginia Polytechnic Institute and State University（弗吉尼亚理工学院暨州立大学）
Autonomous multi-robot optical inspection systems are increasingly applied for obtaining inline measurements in process monitoring and quality control. Numerous methods for path planning and robotic coordination have been developed for static and dynamic environments and applied to different fields. However, these approaches may not work for the autonomous multi-robot optical inspection system due to fast computation requirements of inline optimization, unique characteristics on robotic end-effector orientations, and complex large-scale free-form product surfaces. This paper proposes a novel t…
Minimizing Grid Interconnection Capacity Requirements for AI Data Centers: A Developer-Side Planning Framework with Onsite Resources and Workload Flexibility
In this paper, we relate the feedback capacity of parallel additive colored Gaussian noise (ACGN) channels to a variant of the Kalman filter. By doing so, we obtain lower bounds on the feedback capacity of such channels, as well as the corresponding feedback (recursive) coding schemes, which are essentially power allocation policies with feedback, to achieve the bounds. The results are seen to reduce to existing lower bounds in the case of a single ACGN feedback channel, whereas when it comes to parallel additive white Gaussian noise (AWGN) channels with feedback, the recursive coding scheme r…
In power systems, large-scale optimisation problems are extensively used to plan for capacity expansion at the supra-national level. However, their cost-optimal solutions are often not exploitable by decision-makers who are preferably looking for features of solutions that can accommodate their different requirements. This paper proposes a generic framework for addressing this problem. It is based on the concept of the epsilon-optimal feasible space of a given optimisation problem and the identification of necessary conditions over this space. This framework has been developed in a generic cas…
MRTA-Sim: A Modular Simulator for Multi-Robot Allocation, Planning, and Control in Open-World Environments
Department of Electrical Engineering and Computer Sciences, University of California at Berkeley（加州大学伯克利分校电子工程与计算机科学系）；Toyota Motor North America（丰田北美公司）
Benefited from the advances of deep learning (DL) techniques, deep joint source-channel coding (JSCC) has shown its great potential to improve the performance of wireless transmission. However, most of the existing works focus on the DL-based transceiver design of the JSCC model, while ignoring the resource allocation problem in wireless systems. In this paper, we consider a downlink resource allocation problem, where a base station (BS) jointly optimizes the compression ratio (CR) and power allocation as well as resource block (RB) assignment of each user according to the latency and performa…
The large shares of wind power generation in electricity markets motivate higher levels of operating reserves. However, current reserve sizing practices fail to account for important topological aspects that might hinder their deployment, thus resulting in high operating costs. Zonal reserve procurement mitigates such inefficiencies, however, the way the zones are defined is still open to interpretation. This paper challenges the efficiency of predetermined zonal setups that neglect the location of stochastic power production in the system, as well as the availability, cost and accessibility o…
The key of the post-disaster humanitarian logistics (PD-HL) is to build a good facility location and capacity planning (FLCP) model for delivering relief supplies to affected areas in time. To fully exploit the historical PD data, this paper adopts the data-driven distributionally robust (DR) approach and proposes a novel multi-period FLCP model under the $\infty$-Wasserstein joint chance constraints (MFLCP-W). Specifically, we sequentially decide locations from a candidate set to build facilities with supply capacities, which are expanded if more economical, and use a finite number of histori…
Nearly all types of energy systems (such as power systems, natural gas supply systems, fuel supply systems, and so forth) are going through a major transition from centralised, top-down structures to distributed, clean energy approaches in order to address the concerns regarding climate change, air quality, depletion of natural resources, and energy security, whilst also enabling the supply of energy to communities in line with the goals of sustainable development. Accordingly, the establishment of the concept of sustainable, decentralised, multi-carrier energy systems, together with the decli…
The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Brno University of Technology（布尔诺理工大学）
We study the problem of minimizing the resource capacity of autonomous agents cooperating to achieve a shared task. More specifically, we consider high-level planning for a team of homogeneous agents that operate under resource constraints in stochastic environments and share a common goal: given a set of target locations, ensure that each location will be visited infinitely often by some agent almost surely. We formalize the dynamics of agents by consumption Markov decision processes. In a consumption Markov decision process, the agent has a resource of limited capacity. Each action of the ag…
Energy storage can play an important role in energy management of end users. To promote an efficient utilization of energy storage, we develop a novel business model to enable virtual storage sharing among a group of users. Specifically, a storage aggregator invests and operates the central physical storage unit, by virtualizing it into separable virtual capacities and selling to users. Each user purchases the virtual capacity, and utilize it to reduce the energy cost. We formulate the interaction between the aggregator and users as a two-stage optimization problem. In Stage 1, over the invest…
