Multi-Objective Dynamic Economic Dispatch with Demand Side Management of Residential Loads and Electric Vehicles

In this paper, a multi-objective optimization method based on the normal boundary intersection is proposed to solve the dynamic economic dispatch with demand side management of individual residential loads and electric vehicles. The proposed approach specifically addresses consumer comfort through acceptable appliance deferral times and electric vehicle charging requirements. The multi-objectives of minimizing generation costs, emissions, and energy loss in the system are balanced in a Pareto front approach in which a fuzzy decision making method has been implemented to find the best compromise solution based on desired system operating conditions. The normal boundary intersection method is described and validated

Cost-Constrained Dynamic Optimal Electric Vehicle Charging

Electric vehicles are an integral component of an environmentally sustainable and resilient infrastructure. Successful penetration of electric vehicles requires close coupling between the customers and load serving entities, adaptive energy markets, and technological advancements. In this paper, distribution line over-loading due to vehicle charging has been mitigated using both day-ahead (static) and real-time (dynamic) frameworks, using continuous and discrete charging rates. The proposed solution focuses on valley filling (system perspective) and charging cost reduction (customer perspective). The real-time solution was achieved using a moving horizon optimization technique. In addition to providing charging coordination, the impacts of two different pricing structures were analyzed to ascertain the customer\u27s individual cost optima with respect to the system optima. The results presented strongly indicate that a global pricing structure will not be optimal for all consumers due to their diverse driving habits

Electric Vehicle Scheduling Considering Co-Optimized Customer and System Objectives

Efficient electric vehicle (EV) scheduling is a multi-objective optimization problem with conflicting customer and system operator interests, especially during vehicle-to-grid implementations. Economic charging while minimizing battery degradation and maintaining system load profiles couple the interests of these two entities. This paper focuses on identifying the relationships between these objectives and proposes to use an augmented epsilon-constrain (AUGMECON) based technique to implement two-way and three-way multi-objective optimizations. The importance of using these objectives in peak-shaving and valley-filling for an aggregated (residential) EV fleet is discussed. The proposed solution provides a look-ahead strategy into effective EV scheduling by co-optimizing multiple objectives. To provide operational guidance to utilities and customers, an optimal solution may be selected from those represented by the Pareto fronts

Multi-Objective Electric Vehicle Scheduling Considering Customer and System Objectives

Electric vehicle (EV) scheduling is a multi-objective optimization problem with conflicting system and customer interests. They bear the potential to support the grid while providing incentives to the customers through energy transactions, demand response and grid support. Vehicle-to-grid operations provide the customer with attractive avenues for earning revenues but degrade the battery life. Efficient and economical solutions require a balance between customer incurred costs, battery degradation costs and system health. In this paper, the relationships between these objectives have been explored using a multi-objective optimization technique called augmented epsilon-constraint method (AUGMECON). The Pareto optimal solutions will provide day-ahead strategies for coordinating electric vehicles which can then be used for selecting mutually beneficial outcomes

Economic and Battery Health Conscious Vehicle-to-Grid Electric Vehicle Operation

Maximizing profits through vehicle-to-grid interactions are in conflict with the minimizing battery degradation costs, and thus a major customer concern. A holistic scheduling problem is, therefore, required to consider the impact of the charging power and that of the switching between vehicle-to-grid and grid-to-vehicle modes to account for battery health. In this paper, we propose a mixed-objective approach toward vehicle-to-grid interactions of a residential parking lot complex. The solution makes accommodation for battery health while considering the cost/profit model for the electric vehicles. We discuss the importance of system constraints in achieving both, valley filling and peak shaving. A moving horizon model is used to determine real-time schedules for the vehicles. Customer convenience is the central focus of the proposed methodology