This paper investigates the issues of day-ahead and real-time cooperative energy management for multienergy systems formed by many energy bodies. To address these issues, we propose an event-triggered-based distributed algorithm with some desirable features, namely, distributed execution, asynchronous communication, and independent calculation. First, the energy body, seen as both energy supplier and customer, is introduced for system model development. On this basis, energy bodies cooperate with each other to achieve the objective of maximizing the day-ahead social welfare and smoothing out the real-time loads variations as well as renewable resource fluctuations with the consideration of different timescale characteristics between electricity and heat power. To this end, the day-ahead and real-time energy management models are established and formulated as a class of distributed coupled optimization problem by felicitously converting some system coordinates. Such problems can be effectively solved by implementing the proposed algorithm. With the effort, each energy body can determine its owing optimal operations through only local communication and computation, resulting in enhanced system reliability, scalability, and privacy. Meanwhile, the designed communication strategy is event-triggered, which can dramatically reduce the communication among energy bodies. Simulations are provided to illustrate the effectiveness of the proposed models and algorithm.

In this paper, a novel energy management framework for energy Internet with many energy bodies is presented, which features multicoupling of different energy forms, diversified energy roles, and peer-to-peer energy supply/demand, etc. The energy body as an integrated energy unit, which may have various functionalities and play multiple roles at the same time, is formulated for the system model development. Forecasting errors, confidence intervals, and penalty factor are also taken into account to model renewable energy resources to provide tradeoff between optimality and possibility. Furthermore, a novel distributed-consensus alternating direction method of multipliers (ADMM) algorithm, which contains a dynamic average consensus algorithm and distributed ADMM algorithm, is presented to solve the optimal energy management problem of energy Internet. The proposed algorithm can effectively handle the problems of power-heat-gas-coupling, global constraint limits, and nonlinear objective function. With this effort, not only the optimal energy market clearing price but also the optimal energy outputs/demands can be obtained through only local communication and computation. Simulation results are presented to illustrate the effectiveness of the proposed distributed algorithm.

Distributed algorithms are increasingly being used to solve the economic dispatch problem of integrated energy systems (IESs) because of their high flexibility and strong robustness, but those algorithms also bring more risk of cyber-attacks in IESs. To solve this problem, this article investigates the distributed robust economic dispatch problem of IESs under cyber-attacks. First, as the first line of defense against attacks, a privacy-preserving protocol is designed for covering up some vital information used for economic dispatch of IESs. On this basis, a distributed robust economic dispatch strategy is presented to achieve the energy management of IESs in the presence of misbehaving units, which consists of a neighbor-observe-based detection process and a reputation-based isolation process. The proposed strategy is implemented in a fully distributed fashion and possesses strong robustness against various colluding and noncolluding attacks. In addition, the strategy can not only ensure the reliability of information transmission among energy units, but also solve the problem of incorrect measurement of distributed local load data caused by cyber-attacks. Finally, the effectiveness of the proposed strategy is illustrated by simulation cases on a 39-bus 32-node power–heat IES.

This paper investigates the economic dispatch problem of microgrids in a distributed fashion. To address this issue, a delay-free-based distributed algorithm is presented to optimally assign the whole energy demand among local generation units with the objective of minimizing the agminated operation cost. By implementing the proposed algorithm, each component can find its own optimal operations by only requiring local computation and communication. As a result, it enhances the system robustness, flexibility, privacy, etc. More importantly, the time-varying delays model is considered and embedded into the design of our distributed algorithm, such that the components can employ the delays information to achieve the collaborative operation, which are more general and applicable for practical power systems. In addition, we have proved that the proposed algorithm can converge to the global optimal point under some sufficient conditions. Finally, several simulations are provided to demonstrate the correctness and effectiveness of the proposed algorithm.

The islanded and network-connected modes are expected to be modeled into a unified form as well as in a distributed fashion for multi-energy system. In this way, the adaptability and flexibility of multi-energy system can be enhanced. To this aim, this paper establishes a double-mode energy management model for the multi-energy system. It is formed by many energy bodies. With such a model, each participant is able to adaptively respond to the change of mode switching. Furthermore, a novel distributed dynamic event-triggered Newton-Raphson algorithm is proposed to solve the double-mode energy management problem in a fully distributed fashion. In this method, the idea of Newton descent along with the dynamic event-triggered communication strategy are introduced and embedded in the execution of the proposed algorithm. With this effort, each participant can adequately utilize the second-order information to speed up convergence. The optimality is not affected. Meanwhile, the proposed algorithm can be implemented with asynchronous communication and without needing special initialization conditions. It exhibits better flexibility and adaptability especially when the system modes are changed. In addition, the continuous-time algorithm is executed with discrete-time communication driven by the proposed dynamic event-triggered mechanism.It results in reduced communication interaction and avoids needing continuous-time information transmission. It is also proved that each participant can asymptotically converge to the global optimal point. Finally, simulation results show the effectiveness of the proposed model and illustrate the faster convergence feature of the proposed algorithm.

For the integrated energy system (IES) formed by a cluster of energy hubs (EHs), the outputs of EHs and system parameters, which greatly influence the security performance, should be properly adjusted. In this article, an event-triggered distributed hybrid control scheme is proposed to achieve security and economic operation for the IES. First, the EH output control is designed based on the features of energy networks as well as the containment and consensus algorithms. According to the control of outputs, the electricity and heat load power can be accurately shared without knowing the network parameters. Second, the pressure is bounded within an acceptable range, and the frequency is reverted to the reference value by implementing the proposed control method. Third, an event-triggered communication strategy is employed to design the corresponding protocols, resulting in reduced communication cost. Finally, the control of devices based on the equal incremental principle is proposed to achieve the minimal economic cost for each EH with considering energy prices. The results of numerical case studies are presented to validate the performance of the proposed control method.