In a microgrid, renewable energy sources (RES) exhibit stochastic behavior, which affects the microgrid continuous operation. Normally, energy storage systems (ESSs) are installed on the main branches of the microgrids to compensate for the load-supply mismatch. However, their state of charge (SoC) level needs to be balanced to guarantee the continuous operation of the microgrid in case of RES unavailability. This paper proposes a distributed forecast-based consensus control strategy for DC microgrids that balances the SoC levels of ESSs. By using the load-supply forecast of each branch, the microgrid operational continuity is increased while the voltage is stabilized. These objectives are achieved by prioritized (dis)charging of ESSs based on the RES availability and load forecast. Each branch controller integrates a load forecasting unit based on long short-term memory (LSTM) deep neural network that adaptively adjusts the (dis)charging rate of the ESSs to increase the microgrid endurability in the event of temporary generation insufficiencies. Furthermore, due to the large training data requirements of the LSTM models, distributed extended Kalman filter algorithm is used to improve the learning convergence time. The performance of the proposed strategy is evaluated on an experimental 380V DC microgrid hardware-in-the-loop test-bench and the results confirm the achievement of the controller objectives.

This paper presents a complete design, analysis, and performance evaluation of a novel distributed event-triggered control and estimation strategy for DC microgrids. The primary objective of this work is to efficiently stabilize the grid voltage, and to further balance the energy level of the energy storage (ES) systems. The locally-installed distributed controllers are utilised to reduce the number of transmitted packets and battery usage of the installed sensors, based on a proposed event-triggered communication scheme. Also, to reduce the network traffic, an optimal observer is employed which utilizes a modified Kalman consensus filter (KCF) to estimate the state of the DC microgrid via the distributed sensors. Furthermore, in order to effectively provide an intelligent data exchange mechanism for the proposed event-triggered controller, the publish-subscribe communication model is employed to setup a distributed control infrastructure in industrial wireless sensor networks (WSNs). The performance of the proposed control and estimation strategy is validated via the simulations of a DC microgrid composed of renewable energy sources (RESs). The results confirm the appropriateness of the implemented strategy for the optimal utilization of the advanced industrial network architectures in the smart grids.

A novel distributed secondary layer control strategy based on average consensus and fractional‐order proportional‐integral (FOPI) local controllers is proposed for the regulation of the bus voltages and energy level balancing of the energy storage systems (ESSs) in DC microgrids. The distributed consensus protocol works based on an undirected sparse communication network. Fractional‐order local controllers increase the degree of freedom in the tuning of closed‐loop controllers, which is required for DC microgrids with high order dynamics. Therefore, here, FOPI local controllers are proposed for enhanced energy balancing of ESSs and improved regulation of the bus voltages across the microgrid. The proposed control strategy operates in both islanded and grid‐connected modes of a DC microgrid. In both modes, the average voltage of the microgrid converges to the microgrid desired reference voltage. The charging/discharging of ESSs is controlled independent of the microgrid operating mode to maintain a balanced energy level. The performance of the proposed distributed control strategy is validated in a 38‐ V DC microgrid case study, simulated by Simulink real‐time desktop, consisting of 10 buses and a photovoltaic renewable energy source.

State estimation is one of the main challenges in the microgrids, due to the complexity of the system dynamics and the limitations of the communication network. In this regard, a novel real-time event-based optimal state estimator is introduced in this paper, which uses the proposed adaptive send-on-delta (SoD) non-uniform sampling method over wireless sensors networks. The proposed estimator requires low communication bandwidth and incurs lower computational resource cost. The threshold for the SoD sampler is made adaptive based on the average communication link delay, which is computed in a distributed form using the event-based average consensus protocol. The SoD non-uniform signal sampling approach reduces the traffic over the wireless communication network due to the events transmitted only when there is a level crossing in the measurements. The state estimator structure is extended on top of the traditional Kalman filter with the additional stages for the fusion of the received events. The error correction stage is further improved by optimal reconstruction of the signals using projection onto convex sets (POCS) algorithm. Finally, an Internet of things (IoT) experimental platform based on LoRaWAN and IEEE 802.11 (WiFi) protocols is developed to analyse the performance of the state estimator for the IEEE 5 Bus case study microgrid.

In a thermal plant, the combustion system of the coal-fired boiler is a nonlinear time-varying system, which leads to undesired responses, if traditional proportional-integral-derivative controllers are utilized. Internal process delay is an important characteristic of the steam pressure in the boiler of coal-fired power plants, which affects the closed-loop dynamics. To compensate for this significant delay and reach the desired performance, a novel control strategy based on a combination of an optimal fractional-order proportional-integral-derivative (OFOPID) controller with a Smith predictor (SP) structure is suggested in this paper that achieves the desired control performance required for the optimal operation of the boiler. The simulation results of a coal-fired power plant boiler with the power of 300 MW show that the performance is improved considerably by having lower overshoot and lower rise and settling times, as well as a lower mean square error during the operation of the power plant Boiler.

The privacy of electricity consumers has become one of the most critical subjects in designing smart meters and their proliferation. In this work, a multilayer architecture has been proposed for anonymous data collection from smart meters, which provides: (1) The anonymity of information for third‐party data consumers; (2) Secure communication to utility provider network for billing purposes; (3) Online control of data sharing for end‐users; (4) Low communication costs based on available Internet of things (IoT) communication protocols. The core elements of this architecture are, first, the digital twin equivalent of the cyber‐physical system and, second, the Tangle distributed ledger network with IOTA cryptocurrency. In this architecture, digital twin models are updated in real‐time by information received from trusted nodes of the Tangle distributed network anonymously. A small‐scale laboratory prototype based on this architecture has been developed using the dSPACE SCALEXIO real‐time simulator and open‐source software tools to prove the feasibility of the proposed solution. The numerical results confirm that after a few seconds of anomaly detection, the microgrid was fully stabilized around its operating point with less than 5% deviation during the transition time.