Motivated by recent development in networking and parallel data-processing, we consider a distributed and localized finite-sum (or fixed-sum) allocation technique to solve resource-constrained convex optimization problems over multi-agent networks (MANs). Such networks include (smart) agents representing an intelligent entity capable of communication, processing, and decision-making. In particular, we consider problems subject to practical nonlinear constraints on the dynamics of the agents in terms of their communications and actuation capabilities (referred to as the node dynamics), e.g., networks of mobile robots subject to actuator saturation and quantized communication. The considered distributed sum-preserving optimization solution further enables adding purposeful nonlinear constraints, for example, sign-based nonlinearities, to reach convergence in predefined-time or robust to impulsive noise and disturbances in faulty environments. Moreover, convergence can be achieved under minimal network connectivity requirements among the agents; thus, the solution is applicable over dynamic networks where the channels come and go due to the agent’s mobility and limited range. This paper discusses how various nonlinearity constraints on the optimization problem (e.g., collaborative allocation of resources) can be addressed for different applications via a distributed setup (over a network).

This article takes an integrated view of optimized capacity design and operation of islanded energy hubs. We consider energy hubs that incorporate emerging distributed energy resources as well as energy storage devices and fully support electricity and heat demand of an islanded installation. Both battery and hydrogen storage are incorporated. To explicitly account for the stochasticity in renewable energy generation and load, the energy hub capacity design problem is first expressed as a chance-constrained optimization problem and then reformulated as a robust counterpart problem, where battery charging/discharging responds to stochastic renewable energy generation and load realizations through a control policy. In particular, an affine policy is considered, enabling a linear program formulation of the problem. To reduce conservativeness of the design, an iterative algorithm is proposed, where the interaction between a chance-constrained design problem and a validation problem is achieved through a scalar auxiliary variable. The design result demonstrates a balanced tradeoff between robustness and cost efficiency. After the energy hub has been designed, we propose a bi-level operating strategy, where a day-ahead schedule is optimized at the higher level and model predictive control is used for tracking the schedule in real time at the lower level. Finally, we discuss the potential for increasing the reliability of energy hub systems while decreasing operational cost by sharing energy between multiple energy hubs through networking.