More electric aircrafts (MEAs) comprise a vast amount of power electronic loads, which usually behave as constant power loads (CPLs). The incremental negative impedance of CPLs threatens system stability. To ensure an effective control of power flow in MEAs, eliminating the undesired behavior of CPLs is a necessity. This aim requires spontaneous power estimation of the time-varying uncertain loads. In this paper, an adaptive backstepping controller, which is interconnected to a 3rd degree cubature Kalman filter (CKF), is developed for a DC MG feeding non-ideal CPLs. At first, the load power is considered as an artificial state and augmented into the system states, which enables estimation of not only the DC MG states but also the unknown value of the load power. The estimated load power is then forwarded to a backstepping controller. The systematic approach of this controller allows obtaining the control signal, which is the duty ratio of the switch, to not only system stabilizing but also tracking a desired voltage of the DC bus under the load power variations. The proposed adaptive controller is tested on a DC MG that has one CPL. The conducted experimental results verify the effectiveness of the proposed adaptive controller.

Large solar photovoltaic (PV) penetration using inverters in low voltage (LV) distribution networks may pose several challenges, such as reverse power flow and voltage rise situations. These challenges will eventually force grid operators to carry out grid reinforcement to ensure continued safe and reliable operations. However, smart inverters with reactive power control capability enable PV systems to support voltage quality in the distribution network better. This paper gives an overview of the current state-of-the-art control strategies for handling voltage problems through PV inverters and other devices. In addition, the (control) technical issues of PV systems integrated into the LV distribution network are considered from a control point of view. By comparing the control issues of PV integration into the grid, the paper aims to help distribution system operators to expand the volume of PV generation in the distribution system in an efficient and safe manner. Additionally, it will help control engineers and researchers select proper control strategies for PV systems as well as other distributed renewable sources.

New green energy policies are encouraging district heating (DH) companies to exploit heat production by power-to-heat (P2H) assets e.g. heat pumps (HPs) and electric boilers (EBs). Therefore, they are shifting to exploit these assets in their distribution network besides the traditional central combined heat and power (CHP) units in their production chain. It will lead to an increasingly complex interaction between DH networks and electricity markets. This paper proposes an economic model predictive control (EMPC) framework to investigate how DH companies can manage this extra complexity and if they can contribute in an efficient way towards balancing the production and consumption of electric power in smart grids through playing in the reserve capacity markets. The framework is applied to a real world case study and the results are discussed. The results show the new paradigm increases operation costs. However, reserving a part of the capacity for playing in reserve capacity market will not only make up for the extra costs to some extent, but also improve the capability of providing regulating power.