Building energy consumption has taken up to 40% of global energy use, which substantially increases the resources for greenhouse gas emissions. Among various sectors, the commercial building sector is inevitably more favorable as the pioneer for decarbonization under the current power system structure due to the high peak electricity demand and inefficient energy management policy. This study investigates an efficient strategy for coordinating energy usage for commercial buildings to enhance the power system decarbonization and energy efficiency. First, this article introduces an Office Energy Management system for a grid-connected office building, incorporating electric vehicles, batteries, a lighting system, and a heating, ventilation, and air conditioning system. Then, an adjusted carbon emission flow model for dynamically tracking the carbon footprint is presented to fit the commercial building scenario. Finally, a novel human–machine learning (HML) system, combining human intelligence and reinforcement learning methods, is formulated to optimize energy management strategies. Unlike the traditional artificial intelligence technique, the HML paradigm enables humans and machines to collaborate at the energy management level, significantly improving algorithm performance. The proposed hybrid approach, using the HML method, offers up to 8.54% higher net benefits than the other state-of-the-art reinforcement approach.

Cyberattacks on transmitted signals are the most critical threats to modern microgrid (MG) systems and should be accurately addressed to ensure safe and reliable operation. This article investigates the cybersecurity of dc MGs against false data injection attacks and develops a novel model-based observer system. The proposed projection operator (PO)-based unknown input observer is uniquely designed to detect attacks on the transmitted data from other distributed generation units. For this purpose, a bank of PO observers is developed in each distributed generation unit to estimate all neighbor units' dynamic states. Cyberattack reconstruction compares the remotely observed state values with the measured ones. Afterwards, the detected attack signal values are utilized to restore the integrity of the compromised signals, effectively mitigating the harmful effects of cyberattacks. The most important feature of this scheme, which distinguishes it from similar methods, is that there is no need for a secure channel or additional information transfer in this method. Extensive real-time numerical simulations are performed to assess the practicality and efficiency of the proposed approach when subjected to various attack scenarios, demonstrating its advantages over other methods.

Despite its efficiency, the classical Hopfield network was a highly impractical data-searching solution due to its limited storage capacity. While the recently released modern Hopfield variant has increased its storage capacity, its searching ability is heavily affected by local minima and saddle points, which prevented it from becoming a worthy successor of the classical Hopfield network. We propose a novel unsupervised clustering approach to bypass local minima and saddle points to enhance the overall robustness of the Hopfield network. Our experimental results on benchmark MNIST indicate that our algorithm can increase the retrieval accuracy by over (20%) in general against the Hopfield Update Rule, proving that it is a far superior modelling solution.

Distributed energy sources including renewable energy (RE) sources and electric vehicle (EV) discharging offer opportunities to improve grid performance. Although uncoordinated EV charging can perfectly meet users’ driving needs, it brings great challenges in maintaining the quality of low voltage (LV) distribution grids. Most existing EV control strategies do not take into account the heterogeneous nature of EV charging, including the various users’ travel needs and the intermittent nature of RE sources. This article proposes a hierarchical control method that simultaneously coordinates EV charging by considering a dynamic energy tariff, RE sources, EV hardware characteristics, and particularly the EV’s driving needs. The proposed local controller sends the EV’s charging priority and required energy to the central controller based on the EV’s hardware characteristics and users’ driving needs. The central controller then uses this information along with the energy tariff from the retailer and the present grid performance to control EV’s charging or discharging power. The efficacy of this hierarchical control method is evaluated using an Australian LV grid. The obtained results show that this method can reduce the neutral current and voltage imbalance with the maximized usage of renewable energy resources while the EV users’ driving needs are met.

Increasing penetration of Distributed Generations (Photovoltaic solar energy (PV), Wind energy, and Battery Energy Storage) and PEVs (Plug-in Electric Vehicles) into smart grid induce network imbalance which reduces power quality. The uncertainty of demand-generation requires balancing for mitigating network imbalance. Several researchers have used various optimization methods for mitigating unbalance. Moreover, a few researchers have done comparative studies of optimization methods for mitigating unbalance till now. This paper proposes a method to mitigate unbalance and reduce the total power loss by optimizing load distribution among phases. This paper compares the performance of Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE) algorithms on the application of phase balancing. Finally, the efficacy of these algorithms are evaluated for the proposed unbalance mitigation technique, and it is found that the proposed technique using DE algorithm can reduce a significant amount of unbalance at all the buses of the distribution grid with less computational effort.

This paper presents a new approach of distributed coordinated control for multiple interlinking converters (ILCs) to regulate the power flow across the hybrid AC/DC microgrid composed of AC and DC sub-microgrids (SMGs). These SMGs are linked together by multiple parallel ILCs that are operated as bidirectional power converters. The proposed control system not only allows reliable management and control of the power flow between SMGs but also regulates the DC voltage for the DC SMGs as part of the process. One of the key gains of this structure is the mitigation of the circulating current that results from the parallel operation of the converters. Although the circulating current is more present at the AC side, it can also exist on the DC side. At the AC side, this is achieved through the implementation of the d-q-0 axis control strategy. The presented outer control loop is a modified arrangement that could not only ensure accurate power sharing but also suppress the circulating current at the DC side.

Due to capacity constraints and the uneven distribution of resources, it becomes a trend that multiple microgrids are interconnected as a net structure through energy routers. With the large-scale penetration of distributed energy, the supply mode of energy networks has been gradually transformed into multisource, multipath, and networked supply. In order to adapt to the energy supply mode and the real-time power transaction, a minimum loss routing (MLR) algorithm based on real-time transaction is proposed to realize the end-to-end energy transmission. On the basis of bidding information, the real-time transaction is introduced to determine the source and destination address of power transmission and the amount and transmission time of power flows. Then, an MLR is selected for transaction power, which minimizes the power loss caused by conversion and transmission. As the key of power real-time transaction, the solutions to transmission time difference, single-loop or double-loop power supply modes, and congestion managements are incorporated with the Dijkstra algorithm to find a no-congestion MLR in this paper. Finally, the effectiveness of the proposed optimization algorithm in the selection of MLR and congestion managements is verified by simulations.

The direct matrix converter (MC) is a promising converter that performs direct AC-to-AC conversion. Model predictive control (MPC) is a simple and powerful control strategy for power electronic converters including the MC. However, weighting factor design and heavy computational burden impose significant challenges for this control strategy. This paper investigates the sequential MPC (SMPC) for a three-phase direct MC. In this control strategy, each control objective has an individual cost function and these cost functions are evaluated sequentially based on priority. The complex weighting factor design process is not required and the computational burden can be reduced. In addition, specifying the priority for control objectives can be achieved. A comparative simulation study with standard MPC is carried out in Matlab/Simulink. Control performance is compared to the standard MPC and found to be comparable. Simulation results verify the effectiveness of the proposed strategy.

The distribution power subsystems are usually interconnected to each other, so the design of the interconnected optimal filtering algorithm for distributed state estimation is a challenging task. Driven by this motivation, this paper proposes a novel consensus filter based dynamic state estimation algorithm with its convergence analysis for modern power systems. The novelty of the scheme is that the algorithm is designed based on the mean squared error and semidefinite programming approaches. Specifically, the optimal local gain is computed after minimizing the mean squared error between the true and estimated states. The consensus gain is determined by a convex optimization process with a given suboptimal local gain. Furthermore, the convergence of the proposed scheme is analyzed after stacking all the estimation error dynamics. The Laplacian operator is used to represent the interconnected filter structure as a compact error dynamic for deriving the convergence condition of the algorithm. The developed approach is verified by using the renewable microgrid. It shows that the distributed scheme being explored is effective as it takes only 0.00004 seconds to properly estimate the system states and does not need to transmit the remote sensing signals to the central estimator.

This paper presents a time-delayed model predictive control for power converters used in vehicle to grid and grid to vehicle systems. Finite-based model predictive control has proven to be an alternate digital control method for power converters. However, there are some real-time implementation issues, including specifically time delay, that have to be addressed in order to achieve the system reliability and stability as well as better performance. The proposed method compensates the delay time arising from measuring, calculating, and applying the optimal control sequence in the digital processor. In this way, the delay time is considered in the system input and optimal switching states are applied to the converter once they are available. The proposed method is studied through two benchmarks and verified numerically via MATLAB/Simulink.

In this paper, a direct yaw-moment control (DYC) scheme is proposed for a four-wheel-independently-actuated electric vehicle (FWIA EV). An upper controller is based on a reference model with two degrees of freedom (2-DOF) that generates the desired yaw rate for a sliding mode controller to track so as to improve the vehicle dynamic stability. A torque optimization distribution strategy is designed in the lower controller to allocate the required torques to each in-wheel motor for vehicle stability enhancement. The proposed DYC scheme is implemented in a Carmaker vehicle model and a MATLAB/Simulink control model and evaluated in simulations of a snake-lane-change and a double-lane-change maneuver. The results show that the side slip angle and tire load rate have been reduced, on average, by 2/3 using the DYC system compared with those without control. The cases with DYC also provide better tracking of the desired trajectory and yaw rate with smaller steering angle than those without control.

The three-phase direct matrix converter has been researched exclusively as a direct AC/AC converter, being a competitive alternative to the conventional AC/DC/AC converter. Other possibilities of the matrix converter such as AC/DC, DC/AC and DC/DC conversion still remain unexplored. This paper firstly explores these possibilities and puts forward a concept of the versatile converter. With one matrix converter, different conversion purposes can be accomplished as required. The matrix converter based conversion has some advantages compared with other converters. Model predictive control (MPC) is applied in this work to control the matrix converter to perform the required conversion goals. A generalized model is obtained for all types of conversion in this work. With MPC, different objectives and constraints can be easily included in the control scheme. In addition, the observers are used to reduce the number of voltage and current sensors. Simulation results verify the effectiveness and feasibility of AC/DC, DC/AC and DC/DC conversion with the matrix converter.

This paper presents work on the hysteresis band control for output current regulation in a direct matrix converter in order to drive an induction motor. The hysteresis band controller offers excellent dynamic performance. It has been extensively researched for the voltage source inverter and inverter based drive systems, but it has not been investigated within the context of a matrix converter or a matrix converter based motor drive. Firstly, this paper proposes a fixed-band hysteresis current controller for a matrix converter to track the prescribed current references, and then a sinusoidal-band hysteresis current controller is proposed. Both methods have fast dynamic performance and they inherently integrate the line modulation technique of the virtual rectifier stage into the overall modulation. The extra modulation stage is not required and the surge current is inherently prevented. The sinusoidal-band hysteresis controller demonstrates that it generates lower harmonic content at the expense of the higher average switching frequency. Following this, both methods are tested as a drive for an induction motor with field-oriented control. With the matrix-converter-based drive system, one significant benefit is that the braking chopper is removed due to the bidirectional feature. The methods are simple and have light computation burden. The obtained results demonstrate the effectiveness and feasibility of the proposed scheme. The experimental work is being carried out to support the proposed scheme.

The model predictive control method can be effective for converter control in distributed power generation but requires a large amount of computation, leading to a considerable time delay in the actuation. If the delay is not reflected, the system performance could get worse. This paper presents a two-step (horizon) prediction algorithm of model predictive control technique for grid-tied inverters used in wind turbine systems. The control objectives such as active and reactive power flow and switching loss reduction are reflected in the objective function of the controller. The proposed model predictive control strategy is verified numerically by using MATLAB/Simulink.

Due to dramatically rising energy demand worldwide power system is often run near the operational and technical limits, where unexpected trivial disturbances can cause possibly massive blackouts. Cyber attacks on smart grid communication networks are one of the impending threats to cause large-scale cascading outage. In contrast to the traditional cyber attack protection techniques, this paper presents a recursive systematic convolutional code based defending technique from the signal processing perspective. This code introduces redundancy in the system for protecting the grid information. Furthermore, an optimal control law is designed to stabilize the power network. Specifically, the performance index for control is converted to a convex semidefinite programming problem. The proposed controller can work well for any initial values. The efficacy of the developed approach is verified through numerical simulations. Results show that the proposed strategy has stronger attack protection performance and the controller can stabilize the grid in a fairly short time. This approach provides a fundamental framework for the design of the smart grid energy management system and reliable communication infrastructure scheme with renewable integration applications.

The penetration of renewable distributed energy resources such as wind turbine has been dramatically integrated in distribution networks due to clean, sustainable, and economical green energy. Due to intermittent wind speed, the power generation patterns vary which can risk the distribution network operation. So, it is intrinsically required to monitor the wind turbines in a distributed way. This paper presents an adaptive-then-combine distributed dynamic approach for monitoring the grid under lossy communication link between the wind turbines and energy management system. Firstly, the wind turbine is represented by a state-space linear equation, with sensors deployed to obtain system state information. Based on the mean squared error principle, an adaptive approach is proposed to estimate the local state information. The global estimation is designed by combining estimation results with weighting factors which are calculated by minimizing estimation error covariance based on semi-definite programming. Finally, the convergence analysis indicates that estimation error is gradually decreased, so the estimated state converges to the actual state. The efficacy of the developed approach is verified using the turbine model. The research is valuable for green energy technologies, households and interconnected industrial information societies, and also provides the knowledge towards a smart and secure green energy future.

This work designs a d-q coupling-suppressed PID controller for the transmission line power flow control using a matrix converter. Matrix converters are used as the power flow controller to regulate the active and reactive power flowing through a transmission line in a power system since there is no bulky energy storage element in this structure. Therefore, the volume is reduced, efficiency increased, and lifetime prolonged. Direct and indirect space vector modulation are investigated and compared. Afterwards, a PID controller is designed to control the power flow. By introducing coupling components into the controller, the coupling effects in the system are suppressed. The proposed strategy can control the active and reactive power efficiently and effectively. Controllable power regions are also obtained. MATLAB/Simulink simulations verify the feasibility and effectiveness of the proposed scheme. An experimental prototype is being developed in order to support the proposition.

The penetration of renewable distributed energy resources, such as wind turbine, has been dramatically increased in distribution networks. Due to the intermittent property, the wind power generation patterns vary, which may risk distribution network operations. So, it is intrinsically necessary to monitor wind turbines in a distributed way. This paper presents an adaptive-then-combine distributed dynamic approach for monitoring the grid under lossy communication links between the wind turbines and energy management system. First, the wind turbine is represented by a state-space linear model, with sensors deployed to obtain the system state information. Based on the mean squared error principle, an adaptive approach is proposed to estimate the local state information. The global estimation is designed by combining estimation results with weighting factors which are calculated by minimizing the estimation error covariance based on semidefinite programming. Finally, the convergence analysis indicates that the estimation error is gradually decreased, so the estimated state converges to the actual state. The efficacy of the developed approach is verified using the wind turbine and the IEEE 6-bus distribution system.

In this paper a simple sliding mode controller based on the averaging state space model is proposed for a DC-DC boost converter. It is demonstrated to be easily implemented and has time-variant sliding coefficients. The proposed controller can effectively regulate the output voltage by controlling the switch states (through the dynamic duty cycles) even when the input voltage, load or output command varies. Furthermore the controller is independent of the inductor current and the load, although the load value is needed when designing the sliding coefficients. The constant switching frequency is maintained thus simplifying the design procedure, enhancing the regulation properties and benefiting the filter design. The controller has good dynamic response, overshoot damping and robustness. Comparative simulations are carried in MATLAB/Simulink between the proposed approach and a widely used PID controller to verify the effectiveness and feasibility of the proposed method.