Managing electric vehicles (EVs) charging activities during times when the electrical grid is congested is a challenging task. This work proposes a dynamic, real-time pricing mechanism to accurately allocate more fairness to the billing strategy of EVs charging. The proposed mechanism is based on the concept of the inverse-demand function to reflect instantaneous energy prices in accordance with the microgrids' real-time energy supply. It is composed of two pricing fractions; retail energy price that represents energy cost for non-EVs demand during peak hours, and congested energy price allocated solely for billing EVs charging events during congested timeslots. The proposed methodology is implemented in a hierarchal multi-agent architecture with an optimal energy management system that aims to provide a cost-efficient microgrid operation. The input to the optimization problem are stochastic models that represent day-ahead PV production forecast, EVs energy levels and connectivity times prediction models based on a discrete-time Markov chain. Additionally, a predictive model of daily load demand is also presented based on adaptive Artificial Neural Network (ANN). We develop these models based on historical data for Miami-Dade County, South Florida. Through numerical simulations, we attest that the proposed pricing mechanism achieves significant energy price reduction when compared with results from previous published pricing policies.

The optimal reactive power dispatch (ORPD) problem represents a noncontinuous, nonlinear, highly constrained optimization problem that has recently attracted wide research investigation. This paper presents a new hybridization technique for solving the ORPD problem based on the integration of particle swarm optimization (PSO) with artificial physics optimization (APO). This hybridized algorithm is tested and verified on the IEEE 30, IEEE 57, and IEEE 118 bus test systems to solve both single and multiobjective ORPD problems, considering three main aspects. These aspects include active power loss minimization, voltage deviation minimization, and voltage stability improvement. The results prove that the algorithm is effective and displays great consistency and robustness in solving both the single and multiobjective functions while improving the convergence performance of the PSO. It also shows superiority when compared with results obtained from previously reported literature for solving the ORPD problem.

Energy management and control of hybrid microgrids is a challenging task due to the varying nature of operation between AC and DC components which leads to voltage and frequency issues. This work utilizes a metaheuristic-based vector-decoupled algorithm to balance the control and operation of hybrid microgrids in the presence of stochastic renewable energy sources and electric vehicles charging structure. The AC and DC parts of the microgrid are coupled via a bidirectional interlinking converter, with the AC side connected to a synchronous generator and portable AC loads, while the DC side is connected to a photovoltaic system and an electric vehicle charging system. To properly ensure safe and efficient exchange of power within allowable voltage and frequency levels, the vector-decoupled control parameters of the bidirectional converter are tuned via hybridization of particle swarm optimization and artificial physics optimization. The proposed control algorithm ensures the stability of both voltage and frequency levels during the severe condition of islanding operation and high pulsed demands conditions as well as the variability of renewable source production. The proposed methodology is verified in a state-of-the-art hardware-in-the-loop testbed. The results show robustness and effectiveness of the proposed algorithm in managing the real and reactive power exchange between the AC and DC parts of the microgrid within safe and acceptable voltage and frequency levels.

The real-time operation of the energy management system (RT-EMS) is one of the vital functions of Microgrids (MG). In this context, the reliability and smooth operation should be maintained in real time regardless of load and generation variations and without losing the optimum operation cost. This paper presents a design and implementation of a RT-EMS based on Multiagent system (MAS) and the fast converging T-Cell algorithm to minimize the MG operational cost and maximize the real-time response in grid-connected MG. The RT-EMS has the main function to ensure the energy dispatch between the distributed generation (DG) units that consist in this work on a wind generator, solar energy, energy storage units, controllable loads and the main grid. A modular multi-agent platform is proposed to implement the RT-EMS. The MAS has features such as peer-to-peer communication capability, a fault-tolerance structure, and high flexibility, which make it convenient for MG context. Each component of the MG has its own managing agent. While, the MG optimizer (MGO) is the agent responsible for running the optimization and ensuring the seamless operation of the MG in real time, the MG supervisor (MGS) is the agent that intercepts sudden high load variations and computes the new optimum operating point. In addition, the proposed RT-EMS develops an integration of the MAS platform with the Data Distribution Service (DDS) as a middleware to communicate with the physical units. In this work, the proposed algorithm minimizes the cost function of the MG as well as maximizes the use of renewable energy generation; Then, it assigns the power reference to each DG of the MG. The total time delay of the optimization and the communication between the EMS components were reduced. To verify the performance of our proposed system, an experimental validation in a MG testbed were conducted. Results show the reliability and the effectiveness of the proposed multiagent based RT-EMS. Various scenarios were tested such as normal operation as well as sudden load variation. The optimum values were obtained faster in terms of computation time as compared to existing techniques. The latency from the proposed system was 43% faster than other heuristic or deterministic methods in the literature. This significant improvement makes this proposed system more competitive for RT applications.

Demand side management has a vital role in supporting the demand response in smart grid infrastructure, in the decision-making of energy management, in household applications is significantly affected by the load-forecasting accuracy. This paper introduces an innovative methodology to enhance household demand forecasting based on energy disaggregation for Short Term Load Forecasting. This approach is constructed from Feed-Forward Artificial Neural Network forecaster and a pre-processing stage of energy disaggregation. This disaggregation technique extracts the individual appliances’ load demand profile from the aggregated household load demand to increase the training data window for the proposed forecaster. These proposed algorithms include two benchmark disaggregation algorithms; Factorial Hidden Markov Model (FHMM), Combinatorial Optimization in addition to three adopted Deep Neural Network; long short- term memory (LSTM), Denoising Autoencoder, and a network which regress start time, end time, and average power. The proposed load forecasting approach outperformed the currently available state-of-the-art techniques; namely root mean square error (RMSE), normalized root mean square error (NRMSE), and mean absolute error (MAE).

The fast development of the residential sector regarding the additional integration of renewable distributed energy sources and the modern expansion usage of essential DC electrical equipment may cause severe power quality problems. For example, the integration of rooftop photovoltaic (PV) may cause unbalance, and voltage fluctuation, which can add constraints for further PV integrations to the network, and the deployment of DC native loads with their nonlinear behavior adds harmonics to the network. This paper demonstrates the smart integration of a DC microgrid to the neighborhood low-voltage distribution network (NLVDN). The DC microgrid is connected to the NLVDN through a three-phase voltage source inverter (VSI), in which the VSI works as a distribution static compensator (DSTATCOM). Unlike previous STATCOM work in the literature, the proposed controller of the VSI of the DC smart building allows for many functions: (a) it enables bidirectional active/reactive power flow between the DC building and the AC grid at point of common coupling (PCC); (b) it compensates for the legacy unbalance in the distribution network, providing harmonics elimination and power factor correction capability at PCC; and (c) it provides voltage support at PCC. The proposed controller was validated by Matlab/Simulink and by experimental implementation at the lab.