• Energy Research

This paper investigates the issues of the distributed energy management problem (EMP) of Microgrids in the presence of communication delays. To address this issue, a consensus-based distributed algorithm is proposed to integrate the economic dispatch and demand response, which can optimally assign the energy among generation units and load units with the objective of maximizing the total social welfare of the power system. Different from existing energy management algorithms, a nonuniform time-varying delays model is considered and embedded into the design of our algorithm, such that each unit can achieve collaborative optimization without the requirement of fixed delays information, which has both theoretical merits and practical engineering value for the efficient and stable operation of Microgrids. Moreover, it is proved that the proposed algorithm can converge to the optimal solution under some sufficient conditions. Finally, the correctness and effectiveness of the proposed method are validated by several simulation results.

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.

This paper mainly discuss a problem that how to optimize the ways on electric system dispatching. The GSO algorithm and natural network optimized by genetic are used to solve the problem. The distributed generations and the loads can be seen as producer, requester and random dispersion members which is in the nature. Due to the electric system is a dynamic system, the load forecasting must be done by the natural network. This algorithm is optimized by genetic algorithm to solve the problem that need lots of data. Through previous algorithm can solve the electric system dispatching problem.

In this paper, a novel distributed method is presented to solve combined heat and power economic dispatch problem, which is formulated as a distributed coupled optimization problem. The optimization goal is achieved by establishing two modified consensus protocols with two corresponding feedback parts while satisfying the electrical and heat supply–demand balance. Moreover, an alternating iterative method is proposed to handle the heat-electrical coupling problem existed in the objective function and the feasible operating regions. In addition, the proposed distributed method is implemented by a multi-agent system framework, which only requires local information exchange among neighboring agents. Simulation results obtained on a 16-bus test system are provided to illustrate the effectiveness of the proposed distributed method.

The synthesis of digital twin (DT) technology into smart grid systems has emerged as a promising approach for enhancing operational efficiency, reliability, and resilience in en-ergy management. DTs offer a virtual representation of physical assets and processes, enabling real-time monitoring, predictive analytics, optimization, and control. This survey paper provides an overview of the recent advances in designing DTs to upgrade the smart grid. We mainly focus on aspects of DT concept, DT empowered economic dispatch, DT empowered prediction and diagnosis, and DT empowered frequency and voltage regulations. Finally, we pinpoint potential challenges and conclude this paper.

This paper aims at developing a data-driven optimal control strategy for virtual synchronous generator (VSG) in the scenario where no expert knowledge or requirement for system model is available. Firstly, the optimal and adaptive control problem for VSG is transformed into a reinforcement learning task. Specifically, the control variables, i.e., virtual inertia and damping factor, are defined as the actions. Meanwhile, the active power output, angular frequency and its derivative are considered as the observations. Moreover, the reward mechanism is designed based on three preset characteristic functions to quantify the control targets: (1) maintaining the deviation of angular frequency within special limits; (2) preserving well-damped oscillations for both the angular frequency and active power output; (3) obtaining slow frequency drop in the transient process. Next, to maximize the cumulative rewards, a decentralized deep policy gradient algorithm, which features model-free and faster convergence, is developed and employed to find the optimal control policy. With this effort, a data-driven adaptive VSG controller can be obtained. By using the proposed controller, the inverter-based distributed generator can adaptively adjust its control variables based on current observations to fulfill the expected targets in model-free fashion. Finally, simulation results validate the feasibility and effectiveness of the proposed approach.

This article proposes a multienergy trading market model based on price matching, aiming to foster multienergy collaboration and enhance energy utilization through individual participation. With the ongoing advancements in energy distribution and marketization, the energy Internet necessitates improved applicability and efficiency for personalized energy responses. To address these requirements, a multienergy trading market model is proposed, which enables the avoidance of user information disclosure and guarantees user trading autonomy. In addition, a joint trading mechanism is designed that accounts for multiple time scales and energy types, consequently reducing trading failures caused by overlooking energy transmission processes. By performing the proposed trading mechanism, the market operator can match various energy types using conversion devices, thereby augmenting matching efficiency. An income mechanism is also established to deter the operator from purposefully evading potential trading opportunities for personal gain. To address the proposed model, an improved hierarchical reinforcement learning algorithm is employed, which effectively overcomes challenges associated with large state action spaces and sparse rewards. Numerical examples are provided to confirm the efficacy of the proposed approach.

Under complex sea conditions, the energy demand for each device of renewable-energy ships presents a random situation, which makes the complex energy demand of the ship integrated energy system (SIES) uncertain during ship navigation. To ensure the economical, stable, and efficient operation of the SIES, this paper proposes a distributed stochastic energy management method to solve the energy management problem (EMP). Firstly, a framework for the SIES including both renewable energy and traditional energy is constructed. Based on the energy efficiency operation index (EEOI) and the operation mode of energy supply devices during navigation, the EMP of the SIES is raised. Then, considering the distributed structure and limited computing resources of the SIES, a distributed stochastic energy management method is proposed. Through this method, the disturbances of load demand can be effectively suppressed, and a stable energy supply is provided for devices such as power propellers. Furthermore, it is analyzed that the proposed method can converge to the O(η) (η is the fixed step size of the proposed method) neighborhood of the optimal energy management decision in the mean-square-error sense. Finally, the simulation results verify that the mean-square-error-optimal energy management decision of the SIES can be obtained by the proposed method in different scenarios, and the proposed method can solve the EMP of SIES under complex sea conditions.