This paper proposes a framework and its mathematical model for optimal routing and charging of an electric vehicle fleet for high-efficiency dynamic transit systems, while taking into account energy efficiency and charging price. Based on an extended pickup and delivery problem, an optimization model is formulated from the transit service providers’ perspective and is applied to an electric vehicle (EV) fleet with economically efficient but small batteries in very urbanized areas. It aims to determine the best route from the origin to the final destination for each EV to satisfy the welfare of all passengers (e.g., travel time and passengers’ travel distance), while maximizing the energy efficiency (e.g., by reducing fuel and charging cost), subject to local/global constraints (e.g., EV charging station availability and battery state-of-charge dynamics). This optimization model is solved as a mixed-integer quadratically constrained programming problem. This paper also explores the potential impact of EV fleet of dynamic commuter transit services on electric distribution systems, such as increased average load.

The increase in power outages caused by high-impact low-probability events, such as extreme weather-related climate variation events, is the main reason behind studying power system resilience. However, the concepts of resilience as well as the proactive procedures that can be carried out to address these events are such have not so far been satisfactorily investigated notwithstanding their growing benefits. This paper exhibits a review of the current research on power system resilience; which predominantly focuses on proactive resilience against natural disasters. Firstly, it gives a theoretical framework to acquire insights into power system resilience and its key features. Secondly, it presents frameworks for proactive resilience of power systems with a spotlight on the extreme weather events and their effect. Finally, various strategies for preparing, hardening and enhancing proactive resilience with a focus on microgrids for improving power system resilience are reviewed.

In this paper, we explore the role of emerging energy brokers (middlemen) in a localized event-driven market at the distribution level for facilitating indirect customer-to-customer energy trading. This proposed localized event-driven market does not aim to replace any existing energy service or become the best market model. Instead, it mainly aims to diversify the energy ecosystem at the edge of distribution networks. In light of this philosophy, the market mechanism will provide extra options for customers and prosumers who have the willingness to directly participate in the retail electricity market occasionally, on top of using existing utility services. It also helps in improving market efficiency and encouraging local-level power balance, while taking into account the characteristics of customers’ behavior. The whole energy trading process will be built as a Markov Decision Process (MDP) with some reinforcement learning and data-driven methods applied. Some economic concepts, like search friction, related to this kind of typical search cost involved market model are also discussed.

This paper proposes an accurate secured framework to detect and stop data integrity attacks in wireless sensors networks in microgrids. To this end, an intelligent anomaly detection method based on prediction intervals is introduced to distinguish malicious attacks with different severities during a secured operation. The proposed anomaly detection method is constructed based on the lower and upper bound estimation (LUBE) method to provide optimal feasible prediction intervals over the smart meter readings at electric consumers. It also makes use of the combinatorial concept of prediction intervals to solve the instability issues arising from the neural networks. Due to the high complexity and oscillatory nature of the electric consumers data, a new modified optimization algorithm based on symbiotic organisms search (SOS) is developed to adjust the NN parameters. The high accuracy and satisfying performance of the proposed model are assessed on the practical data of a residential microgrid.

This paper proposes a novel consensus-based distributed control algorithm for solving the economic dispatch problem of distributed generators. A legacy central controller can be eliminated in order to avoid a single point of failure, relieve computational burden, maintain data privacy, and support plug-and-play functionalities. The optimal economic dispatch is achieved by allowing the iterative coordination of local agents (consumers and distributed generators). As coordination information, the local estimation of power mismatch is shared among distributed generators through communication networks and does not contain any private information, ultimately contributing to a fair electricity market. Additionally, the proposed distributed algorithm is particularly designed for easy implementation and configuration of a large number of agents in which the distributed decision making can be implemented in a simple proportional-integral (PI) or integral (I) controller. In MATLAB/Simulink simulation, the accuracy of the proposed distributed algorithm is demonstrated in a 29-node system in comparison with the centralized algorithm. Scalability and a fast convergence rate are also demonstrated in a 1400-node case study. Further, the experimental test demonstrates the practical performance of the proposed distributed algorithm using the VOLTTRON™ platform and a cluster of low-cost credit-card-size single-board PCs.

In this paper, we propose a new framework for the optimal virtual power plant (VPP) energy management problem considering correlated demand response (CDR). Our objective is to minimize the VPP operating cost while maintaining the power quality of the system. We formulate a risk-constrained two-stage stochastic program to address uncertainties in day-ahead and real-time electricity prices, renewable energy source’s (RES) generation processes, and the CDR relationship. The VPP can also maintain cooling and heating balances by coordinating combined cooling, heating, and power production (CCHP) and CDR units. Extensive simulation results show that the VPP can minimize the operating cost and ensure the energy balance and power quality by coordinating components in the framework we propose.