Biblio

As a part of the global decarbonization agenda, the electrification of the transport sector involving the large-scale integration of electric vehicles (EV) constitues one of the key initiatives. However, the introduction of EV loads results in more variable electrical demand profiles and higher demand peaks, challenging power system balancing, voltage and network congestion management. In this paper, a novel optimal load scheduling approach for a coupled power and transportation network is proposed. It employs an EV charging demand forecasting model to generate the temporal-spatial distribution of the aggregate EV loads taking into account the uncertainties stemmed from the traffic condition. An AC optimal power flow (ACOPF) problem is formulated and solved to determine the scheduling decisions for the EVs, energy storage units as well as other types of flexible loads, taking into account their operational characteristics. Convex relaxation is performed to convert the original non-convex ACOPF problem to a second order conic program. Case studies demonstrate the effectiveness of the proposed scheduling strategy in accurately forecasting the EV load distribution as well as effectively alleviating the voltage deviation and network congestion in the distribution network through optimal load scheduling control decisions.

Demand response analysis in smart grid deployment substantiated itself as an important research area in recent few years. Two-way communication between utility and users makes peak load reduction feasible by delaying the operation of deferrable appliances. Flexible appliance rescheduling is preferred to the users compared to traditional load curtailment. Again, if users' preferences are accounted into appliance transferring process, then customers concede a little discomfort to help the utility in peak reduction. This paper presents a novel Utility-User Cooperative Algorithm (UUCA) to lower total electricity cost and gross peak demand while preserving users' privacy and preferences. Main driving force in UUCA to motivate the consumers is a new cost function for their flexible appliances. As a result, utility will experience low peak and due to electricity cost decrement, users will get reduced bill. However, to maintain privacy, the behaviors of one customer have not be revealed either to other customers or to the central utility. To justify the effectiveness, UUCA is executed separately on residential, commercial and industrial customers of a distribution grid. Harmony search optimization technique has proved itself superior compared to other heuristic search techniques to prove efficacy of UUCA.