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.

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.

Due to the rapidly-changing technologies in the power industry, many new references addressing the frameworks and business models of the next-generation retail electricity market are entering the research community. In particular, considering new customers with considerable demand response awareness and so-called prosumers with localized power generation based on distributed energy resources (DERs), the next-generation retail electricity market infrastructure will be a level playing field for local energy transactions, strategic pricing scheme design, new business model design and building an innovative energy ecosystem. Consequently, there is an urgent need to keep track of international experiences and activities taking place in the field of the market mechanism design problem at the distribution level. This paper provides a comprehensive survey of recent technology developments and aims to inspire awareness of the further deregulation of the electricity market, especially in areas close to customers. We mainly bring attention to the more than 90 articles published during the past five years. The collected literature has been divided into different sections to discuss different aspects of the next-generation retail electricity market under the deregulated power industry.

This paper proposes an innovative economic and engineering coupled framework to encourage typical flexible loads or load aggregators, such as parking lots with high penetration of electric vehicles, to participate directly in the real-time retail electricity market based on an integrated eVoucher program. The integrated eVoucher program entails demand side management, either in the positive or negative direction, following a popular customer-centric design principle. It provides the extra economic benefit to end-users and reduces the risk associated with the wholesale electricity market for electric distribution companies (EDCs), meanwhile improving the potential resilience of the distribution networks with consideration for frequency deviations. When implemented, the eVoucher program allows typical flexible loads, such as electric vehicle parking lots, to adjust their demand and consumption behavior according to financial incentives from an EDC. A distribution system operator (DSO) works as a third party to hasten negotiations between such parking lots and EDCs, as well as the price clearing process. Eventually, both electricity retailers and power system operators will benefit from the active participation of the flexible loads and energy customers.