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This paper considers a class of multi-agent distributed convex optimization with a common set of constraints and provides several continuous-time neurodynamic approaches. In problem transformation, l 1 and l 2 penalty methods are used respectively to cast the linear consensus constraint into the objective function, which avoids introducing auxiliary variables and only involves information exchange among primal variables in the process of solving the problem. For nonsmooth cost functions, two differential inclusions with projection operator are proposed. Without convexity of the differential inclusions, the asymptotic behavior and convergence properties are explored. For smooth cost functions, by harnessing the smoothness of l 2 penalty function, finite- and fixed-time convergent algorithms are provided via a specifically designed average consensus estimator. Finally, several numerical examples in the multi-agent simulation environment are conducted to illustrate the effectiveness of the proposed neurodynamic approaches. [ABSTRACT FROM AUTHOR]

This paper addresses an event-triggered distributed convex optimisation problem over networks with time-varying delays. The communication between agents is triggered by conditions monitored by nodes. The proposed triggering condition is decentralised and does not require continuous communications among agents to calculate the threshold. By constructing a new Lyapunov–Krasovskii function, the optimisation problem is solved, and the explicit sufficient condition for the maximum admissible time delay is derived. Moreover, the Zeno behaviour of the closed-loop system is excluded. Finally, a simulation example is provided to verify the results obtained in this paper. [ABSTRACT FROM AUTHOR]

• The data-driven inverse optimization corrects the DR execution online. • The proposed strategy is realized in a distributed manner and free of initialization. • This paper provides rigorous stability proof for the proposed optimization. • D-PPDS algorithm solves the DR optimization with global inequality constrain. This paper provides an insight into the demand response (DR) optimization in distribution markets consisting of a retailer and multiple demand response aggregators (DRA), where a retailer determines DR incentives based on power consumption profile. Conventional DR optimization with global states and constraints is intractable to be implemented in a distributed framework, which restricts the application feasibility and the potential profit of DR. To handle these limitations, we design a multi-agent architecture for distributed demand response (DDR). An online data-mining method is developed to identify the characteristics of DR. A leader–follower structure decomposes the original problem into a leader problem with global variables and aggregators of sub-problems, where discrete singular consensus is designed to broadcast the leader's strategy to followers in real-time. The distributed perturbation primal–dual sub-gradient (D-PPDS) algorithm is proposed to solve the DDR problem with global inequality constraints in a completely distributed fashion. The proposed DDR strategy is tested by an actual case. The simulation results demonstrate that the asynchronous D-PPDS algorithm can obtain the near-optimal solution of the problem with global inequality constraints, and is robust against delay or plug-and-play. [ABSTRACT FROM AUTHOR]

To achieve large-scale generalized demand-side resources to participate in the "peak-shaving and valley-filling" of the power system, a distributed cooperative control strategy for discrete-time linear multi-agents considering historical data is proposed, which comprehensive consider the changes in load commands, topology, multi-objective function coefficients, communication delays and input delays. This paper combines the distributed multi-agent system with the hierarchical architecture to solve the problems of extensive data calculations in centralized control, which may easily cause communication channel blockage and delay. Considering the cost of changing the generalized demand-side resources and the user's comfort, the pre-learning historical data storage strategy based on K-means clustering is adopted before the actual task, and the idea of transfer learning is introduced into the consensus algorithm. Finally, through experimental statistics, the number of iteration steps of the consensus algorithm is reduced and the effectiveness of the proposed algorithm is proved. [ABSTRACT FROM AUTHOR]

Condition-based and predictive maintenance enable early detection of critical system conditions and thereby enable decision makers to forestall faults and mitigate them. However, decision makers also need to take the operational and production needs into consideration for optimal decision-making when scheduling maintenance activities. Particularly in network systems, such as power grids, decisions on the maintenance of single assets can affect the entire network and are, therefore, more complex. This paper proposes a bi-level multi-agent decision support system for the generation maintenance decision (GMS) of power grids in an electricity market in the context of predictive maintenance. The GMS plays an important role in increasing the reliability at the network level. The aim of the GMS is to minimize the generation cost while maximizing the system reliability. The proposed framework integrates a central coordination system, i.e. the transmission system operator (TSO), and distributed agents representing power generation units that act to maximize their profit and decide about the optimal maintenance time slots while ensuring the energy balance. In the proposed bi-level approach, n upper levels and one lower level (i.e. sub-problems) are solved by the independent agents and by the TSO, respectively. We derive the optimal strategy of the agents that are subject to predictive maintenance via a distributed algorithm, through which agents make optimal maintenance decisions and communicate them to the central coordinator, i.e. the TSO. The TSO decides whether to accept the agents' decisions by considering market reliability aspects and power supply constraints. To solve this coordination problem, we propose a negotiation algorithm using an incentive signal to coordinate the agents' and the central system's decisions, such that all the agents' decisions can be accepted by the central system. We demonstrate the effectiveness of the proposed algorithm with reference to the IEEE 39 bus system. • A bi-level negotiation algorithm for the predictive maintenance of generation units. • The proposed mechanism provides a weak budget balance during negotiation. • The induced mechanism is individually rational for each of the agents. • The performance of the algorithm is investigated based on the IEEE 39 bus system. [ABSTRACT FROM AUTHOR]