The energy crises and growing environmental concerns in modern society have prompted the development of interconnected microgrids (IMGs) that collaboratively control local generation, energy storage, and energy trading to ensure a reliable supply to critical loads. The primary purpose of this paper is to present a real-time energy trading scheme for IMGs that employs Lyapunov Optimization (LO) and accounts for two aspects: resilience to extreme events and awareness of battery degradation. In this study, IMGs incorporate wind and solar energy sources, which are equipped with a battery energy storage system. One important feature of the system is its ability to operate in island mode while enabling inter-microgrid power exchange. This paper proposes the LO framework for identifying the optimal energy trading plan and maximizing individual MG profits. Recognizing the stochastic nature of MG operations over a 24-hour horizon, we employ an LO method to decompose the problem into feasible single-step subproblems, thereby enabling real-time decision-making under uncertainty. The practicality of the proposed framework is demonstrated through six case studies: a base case scenario; outage-aware resilience, with and without battery degradation; and a comparative and scalability analysis of LO using a model predictive control approach. The results indicate that the proposed LO-based IMG achieves better market efficiency and battery health preservation, reduced grid dependency, faster computation, resilience improvement, and cost-effective performance.
This paper studies a space-ground computing-electricity microgrid for low-carbon artificial-intelligence data center (AIDC) operation and space-borne sensing tasks, where ground-source flexible workloads can be offloaded to satellites and satellite-originated (sky-source) tasks can be processed cooperatively in space and on the ground. A bi-level optimization model is developed by explicitly modeling the availability of multi-orbit satellite computing resources. The model characterizes the heterogeneous illumination, visibility, thermal, and latency features of normal low-Earth-orbit (N-LEO), dawn-dusk low-Earth-orbit (DD-LEO), geostationary-Earth-orbit (GEO), and polar-orbit low-Earth-orbit (P-LEO) satellites, and links them with ground-source workload classes, sky-source task modes, ground cooling demand, and renewable-energy capacity planning. The upper level optimizes wind and PV capacities with economic and carbon-emission objectives, while the lower level coordinates grid purchase, energy storage, ground-source offloading, and sky-source mode allocation. Simulation results show that multi-orbit coordination shifts and extends the Pareto front, thereby broadening the feasible tradeoff boundary between economy and low-carbon operation. Nominally, the design meets the modeled high-timeliness equivalent-energy SLA and handles 72.72% of all sky-source demand on orbit; stress tests report ground fallback, unfinished energy, and SLA relaxation separately. The results indicate that multi-orbit space computing can relieve AIDC energy-supply and cooling pressure while enhancing time-critical space-borne task support.
To enhance the frequency stability of renewable energy power systems, it is essential to provide inertia support through battery energy storage systems (BESSs). However, the inertial response characteristics of synchronous generators (SGs), grid-following (GFL) BESS, and grid-forming (GFM) BESS vary significantly, which could lead to complex system frequency characteristics and thereby challenging the spatial distribution of inertia. Given this background, this paper establishes a frequency spatial distribution model (FSDM) that explicitly formulates the distinct control mechanisms within an analytical spatial framework. We develop a virtual inertia spatial optimization model (VISOM) and introduce a mixed- integer tree search algorithm (MITSA) to solve the optimization model. The proposed solution provides an economically optimal distribution of GFL and GFM BESSs. The case studies which are conducted on a modified IEEE 118-bus system validate the correctness and effectiveness of the proposed method for devising spatial virtual inertia for renewable energy power systems.
This article mainly focuses on the issue of event-triggered secure load frequency control (LFC) for multi-area power systems under denial-of-service (DoS) attacks based on input-output data. In contrast to prior results, the issues of controller design and security analysis are solved by considering the characteristics of the DoS attacks, instead of employing average dwell time approach. By analyzing the interaction between attack patterns and event-triggered mechanisms, the valid and invalid durations within attack periods that actually impact the system are defined. Based on which, an attack compensation recovery strategy is proposed within DoS attack intervals under the model-free adaptive control framework. Then, the data-driven controller and parameter estimation are reconstructed using the last successfully triggered signal before an attack occurs, effectively mitigating the impact of communication blockages. Furthermore, a sufficient condition for ensuring the boundedness of tracking errors is derived through recursive analysis, which directly constrain the ratio of valid to invalid time during attack periods. Finally, simulations are provided to validate the effectiveness of the proposed method for multi-area power systems.
As electric power networks (EPNs) and district heating networks (DHNs) become increasingly interconnected, coordinated state estimation (CSE) plays a key role in integrated electricity and heating systems (IEHSs). In DHNs, the operating conditions involve both hydraulic processes and thermal processes, where not only temperatures but also mass flow may vary. To address the resulting variable mass flow (VM) thermal dynamics, a CSE method for IEHSs based on an improved thermal circuit method (TCM-VM) is proposed in this paper. The TCM-VM is developed through coordinate transformation and heat-loss correction, which allows pipeline thermal dynamics under variable mass flow conditions to be modeled in the frequency domain within a transformed coordinate system. Moreover, the EPN states, DHN hydraulics, and network topology constraints are established in the time domain. The CSE problem is addressed using the minimum information loss (MIL) framework to combine electrical and thermal measurements with different sampling intervals. The proposed method is validated under both constant and variable mass flow conditions, and the effects of the measurement configurations, window lengths, and noise levels are assessed. The effectiveness of the proposed method is evaluated through simulations on both a small-scale system and a large-scale IEHS.
