• Energy Research

The distributed frequency control system of microgrids, which relies on classical communication networks between distributed generations (DGs) for frequency regulation and restoration, is vulnerable to cyber-attacks. Quantum distributed controllers offer a secure quantum communication scheme but are less efficient because of continuous communication in quantum systems. This paper proposes a quantum distributed event-triggered secondary frequency control strategy for the islanded AC microgrid. The suggested event-triggered control significantly lessens the communication load and is Zeno-free. Furthermore, a novel false data injection attack (FDIA) scenario is introduced for the quantum-microgrid system. The non-periodic nature of communication can be exploited to directly identify and isolate compromised communication links, thereby enhancing the resilience of the quantum-microgrid system. Finally, simulation results on an AC microgrid with four DGs validate the effectiveness of the suggested control scheme.

The smart grid emphasizes interaction between grid-load-generation, and the electric vehicle (EV) is a kind of favorable load with controllable ability. These EVs could participate in frequency regulation when the grid is suffering from imbalance between supply and demand. According to the requirement of power dispatching center, these EVs could inhibit charging and postpone the electricity demand to other time. It could also transmit electrical power to grid reversely. However, individually frequency response of each independent EV would cause excessive communication flows, and create new impact to power grid. This paper based on cloud service, assembled every EV in an area into a cluster agent, and proposed a kind of distributed cooperative control strategy to harmonize their frequency response. Firstly, the power dispatching center issues the demand request to a leader node named cooperative pinning node. Then, such pinning node releases the adjustment demand to the net work consisted of EV cluster agent nodes. Through communication and calculation on each agent nodes, the goal of fair EV load shedding would reach. Case study shows that the method could organize multiple EV cluster agents effectively and the system would be regulated faster than the original system with vehicle-to-grid participating.

In the power demand side, responsive loads can provide fast regulation and ancillary services as reserve capacities in interconnected power systems. This paper presents a distributed pinning demand side control (DSC) strategy for coordinating multiple load aggregators, i.e., aggregated responsive loads, to provide frequency regulation services. Specifically, a leader-following communication protocol is considered for the load aggregators in which there is a centralized pinner (leader) and multiple load aggregators (followers). The regulation objective is generated from the pinner and only shared with a small fraction of load aggregators. Moreover, a multi-step algorithm is proposed to determine the control gains in the DSC, which not only guarantees the stability of the close-loop system, but also restrains the plant disturbance. Furthermore, the distributed pinning DSC algorithm is integrated into the traditional centralized proportional-integral-based automatic generation control (AGC) framework, which has formed the coupled secondary frequency control structure. It has been shown that the total power mismatch in each control area is shared with both AGC units and load aggregators, and the system frequency can be improved by considering the distributed pinning DSC for load aggregators. Finally, simulation results are provided to demonstrate the effectiveness of the proposed coupled frequency control strategy.

This paper investigates the dynamical behaviors for a four-dimensional energy resource system with time delay, especially in terms of equilibria analyses and Hopf bifurcation analysis. By setting the time delay as a bifurcation parameter, it is shown that Hopf bifurcation would occur when the time delay exceeds a sequence of critical values. Furthermore, the stability and direction of the Hopf bifurcation are determined via the normal form theory and the center manifold reduction theorem. Numerical examples are given in the end of the paper to verify the theoretical results.

Demand response flexible loads can provide fast regulation and ancillary services as reserve capacity in power systems. This paper proposes a joint optimization dispatch control strategy for source-load system with stochastic renewable power injection and flexible thermostatically controlled loads (TCLs) and plug-in electric vehicles (PEVs). Specifically, the optimization model is characterized by a chance constraint look-ahead programming to maximal the social welfare of both units and load agents. By solving the chance constraint optimization with sample average approximation (SAA) method, the optimal power scheduling for units and TCL/PEV agents can be obtained. Secondly, two demand response control algorithms for TCLs and PEVs are proposed respectively based on the aggregate control models of the load agents. The TCLs are controlled by its temperature setpoints and PEVs are controlled by its charging power such that the DR control objective can be fulfilled. The effectiveness of the proposed dispatch and control algorithm has been demonstrated by the simulation studies on a modified IEEE 39 bus system with a wind farm, a photovoltaic power station, two TCL agents and two PEV agents.

A reliable and economic/environmental smart grid (SG) has been a major focus area for modern power system design, which is threatened by the renewable resource penetration and load fluctuation. This paper presents a reliable and economic load frequency control (LFC) strategy based on model predictive control (MPC) to allocate active power set points for different generators by considering units' economic costs and very short-term load forecasting (VSTLF). The MPC-based LFC strategy can balance the economic costs among generation units and take optional control actions in advance by considering time-varying load disturbance over a future time horizon. A comparison with the traditional LFC and the MPC strategy without considering load prediction and economic costs is made in a MATLAB/Simulink scenario for various load disturbances. The simulation results show the effectiveness of the proposed strategy in stabilizing system frequency and reducing response time.

ABSTRACTDistributed robust optimization algorithms focus on developing decision‐making strategies that can operate effectively under uncertain conditions. This paper examines a scenario‐based distributed robust optimization algorithm for optimal scheduling of virtual power plants (VPPs). The proposed algorithm follows three key steps: scenario sampling, scenario reduction, and distributed optimization using the Alternating Direction Multiplier Method (ADMM). This approach balances robustness with computational complexity and ensures convergence, offering a practical solution for multi‐agent optimization. By employing an uncertainty set to represent the variabilities of wind and photovoltaic power generation, which leads to the establishment of a distributed robust optimization model for optimal virtual power plant scheduling. Experimental simulations validate the algorithm's feasibility and efficacy in economically optimal scheduling, offering methodological support for enhancing both robustness and economic efficiency in VPPs' operations.

With the development of strong and smart grid, power grid and communication system become highly interconnected and mutually dependent. Complex network approach is applied in this paper to describe the interactive characteristics of hybrid power and communication systems in terms of topologies. Qualitative analysis of the impact of communication system on power grid is straightforward. However, quantitative analysis is challenge. This paper proposes a generic and systematic approach for quantitatively studying the impact of communication failures on power control system's measurement and control functions.