With the emergence of distributed energy resources (DERs), with their associated communication and control complexities, there is a need for an efficient platform that can digest all the incoming data and ensure the reliable operation of the power system. The digital twin (DT) is a new concept that can unleash tremendous opportunities and can be used at the different control and security levels of power systems. This paper provides a methodology for the modelling of the implementation of energy cyber-physical systems (ECPSs) that can be used for multiple applications. Two DT types are introduced to cover the high-bandwidth and the low-bandwidth applications that need centric oversight decision making. The concept of the digital twin is validated and tested using Amazon Web Services (AWS) as a cloud host that can incorporate physical and data models as well as being able to receive live measurements from the different actual power and control entities. The experimental results demonstrate the feasibility of the real-time implementation of the DT for the ECPS based on internet of things (IoT) and cloud computing technologies. The normalized mean-square error for the low-bandwidth DT case was 3.7%. In the case of a high-bandwidth DT, the proposed method showed superior performance in reconstructing the voltage estimates, with 98.2% accuracy from only the controllers’ states.

Direct Current (DC) microgrids have the potential to improve efficiency and reliability of power system operations in many applications. Droop control has been introduced as one of the most popular strategies. However, basic characteristics of different types of droop control have not been fully examined. While there are a lot of work about the voltage-current (VI) and current-voltage (IV) droop characteristics, analysis of voltagepower (VP) and power-voltage (PV) has not been fully considered and compared for low voltage DC networks. This paper provides the comparative analysis, testing, and performance evaluation of VI, VP, PV, IV droops for buck converter-based DC microgrids. The analytical and simulation results provide a picture of similarities and differences among the four droops. Instead of conventional conception of similar VI and VP, as well as IV and PV, their steady state performance and stability assessment results are different. Ranking of stability of the four droops are verified by Middlebrook criterion and passivity-based stability assessment.

Commercial buildings spend vast amount of money every year on their electricity consumption. Though optimal scheduling and control of commercial buildings had been widely discussed in the literature, coordination of multiple buildings connected to power networks still need further consideration. Privacy issue is a major consideration to building managers, which makes the coordination problem very challenging. This article first provides a convex formulation for the optimal operation of heating, ventilation, and air conditioning systems in commercial buildings. Then, this article proposes the use of the alternating direction method of multiplier to enable privacy-based coordination of multiple commercial buildings in the electric grid. The formulated problem considers the thermal discomfort of the occupants and the building power consumption on the one hand, and the power losses of the power network on the other hand. The algorithm uses minimal amount of exchanged data to avoid any privacy invasion. A case study of multiple commercial buildings is done on an IEEE 15-bus distribution system to verify the proposed problem formulation. The results showed that the algorithm can achieve comparable performance to the centralized case in terms of minimizing the thermal discomfort, power consumption, and power losses.

With the growth of commercial building sizes, it is more beneficial to make them “smart” by controlling the schedule of the heating, ventilation, and air conditioning (HVAC) system adaptively. Single-building-based scheduling methods are more focused on individual interests and usually result in overlapped schedules that can cause voltage deviations in their microgrid. This paper proposes a decentralized management framework that is able to minimize the total electricity costs of a commercial microgrid and limit the voltage deviations. The proposed scheme is a two-level optimization where the lower level ensures the thermal comfort inside the buildings while the upper level consider system-wise constraints and costs. The decentralization of the framework is able to maintain the privacy of individual buildings. Multiple data-driven building models are developed and compared. The effect of the building modeling on the overall operation of coordinated buildings is discussed. The proposed framework is validated on a modified IEEE 13-bus system with different connected types of commercial buildings. The results show that coordinated optimization outperforms the commonly used commercial controller and individual optimization of buildings. The results also show that the total costs are greatly affected by the building modeling.

Heating, Ventilation and Air Conditioning (HVAC) systems consume a large amount of building energy consumption. HVAC systems have multiple varieties and configurations. One type of commercial HVAC systems that has not received enough attention is air-water systems. These systems are commonly used in office buildings and hotels where a higher degree of temperature control is needed. Few works have considered the modeling and optimization of this type of system. This paper aims at providing a convex model for an air-water HVAC system. Moreover, a way to compensate for modeling and approximation errors and handling the uncertainties is provided. This is achieved through the use of the adjustable robust optimization theory. The aim of the optimization is to minimize the energy consumption while ensuring the temperature comfort. Both deterministic and robust formulations are provided, tested, and compared. The results showed that the temperature comfort can be ensured while considering modeling approximations and uncertainties.

Integrating a large number of electric vehicles (EVs) represent a huge challenge to the electric utility distribution system. Therefore, proper control of EV charging is crucial for smooth and practical operation in these systems. This paper proposes an autonomous charge controller that takes a number of these practical issues into consideration. The controller prevents grid under-voltage issues and shaves the system loading profile while achieving good charging levels among different EVs connected to the system. It also takes into consideration the customers' preferences. The proposed controller is based on linear and explicit formulas that are derived and presented in this paper. Moreover, the effect of increased EV penetration on the proposed controller, was studied. The performance of the developed controller was tested by means of simulation and compared with the presented ones in literature. The results showed that the controller can accommodate increased penetration of EVs in distribution systems.