A Robust Optimization Model to the Day-Ahead Operation of an Electric Vehicle Aggregator Providing Reliable Reserve (original) (raw)
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Energy, 2017
Plug-in electric vehicles are expected to play a major role in the transportation system as the environmental problems and energy crisis are being more and more urgent recently. Implementing a large number of vehicles with proper control could bring an opportunity of large storage and flexibility for power systems. The plug-in electric vehicle aggregator is responsible for providing power and controlling the charging pattern of the plug-in electric vehicles under its contracted area. This paper deals with the problem of optimal scheduling problem of plug-in electric vehicle aggregators in electricity market considering the uncertainties of market prices, availability of vehicles and status of being called by the ISO in the reserve market. The impact of the market price and reserve market uncertainties on the electric vehicle scheduling problem is characterized through a stochastic programming framework. The objective of the aggregator is to maximize its profit by charging the plugin electric vehicles on the low price time intervals as well as participating in ancillary service markets. The operational constraints of plug-in electric vehicles and constraints of vehicle to grid are modeled in the proposed framework. An illustrative example is provided to confirm the performance of the proposed model.
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IEEE Transactions on Smart Grid, 2017
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The rapid escalation in plug-in electric vehicles (PEVs) and their uncoordinated charging patterns pose several challenges in distribution system operation. Some of the undesirable effects include overloading of transformers, rapid voltage fluctuations, and over/under voltages. While this compromises the consumer power quality, it also puts on extra stress on the local voltage control devices. These challenges demand for a well coordinated and power network-aware charging approach for PEVs in a community. This paper formulates a real-time electric vehicle charging scheduling problem as an mixed-integer linear program (MILP). The problem is to be solved by an aggregator, that provides charging service in a residential community. The proposed formulation maximizes the profit of the aggregator, enhancing the utilization of available infrastructure. With a prior knowledge of load demand and hourly electricity prices, the algorithm uses a moving time horizon optimization approach, allowi...
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IEEE PES ISGT Europe 2013, 2013
This paper proposes a new cooperative scheduling framework for demand response aggregators (DRAs) and electric vehicle aggregators (EVAs) in a day-ahead market. The proposed model implements the information-gap decision theory (IGDT) to optimize the scheduling problem of the aggregators, which guarantees to obtain the predetermined profit by the aggregators. In the proposed model, the driving pattern of electric vehicle owners and the uncertainty of day-ahead prices are simulated via scenario-based and a bi-level IGDT based methods, respectively. The DR aggregator provides DR from two demand-side management programs including time-of-use (TOU) and reward-based DR. Then, the obtained DR is offered into day-ahead markets. Furthermore, the EVA not only meet the EV owners' demand economically, but also participates in the day-ahead mark while willing to set DR contracts with the DR aggregator. The objective function is to maximize the total profit of DR and EV aggregators perusing two different strategies to face with price uncertainty, i.e., riskseeker strategy and risk-averse strategy. The proposed plan is formulated in a risk-2 based approach and its validity is evaluated on a case study with realistic data of electricity markets.
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, 2021
A community integrated energy system (CIES) with an electric vehicle charging station (EVCS) provides a new way for tackling growing concerns of energy efficiency and environmental pollution, it is a critical task to coordinate flexible demand response and multiple renewable uncertainties. To this end, a novel bi-level optimal dispatching model for the CIES with an EVCS in multi-stakeholder scenarios is established in this paper. In this model, an integrated demand response program is designed to promote a balance between energy supply and demand while maintaining a user comprehensive satisfaction within an acceptable range. To further tap the potential of demand response through flexibly guiding users’ energy consumption and electric vehicles’ behaviors (charging, discharging and providing spinning reserves), a dynamic pricing mechanism combining time-of-use and real-time pricing is put forward. In the solution phase, by using sequence operation theory (SOT), the original chance-constrained programming (CCP) model is converted into a readily solvable mixed-integer linear programming (MILP) formulation and finally solved by CPLEX solver. The simulation results on a practical CIES located in North China demonstrate that the presented method manages to balance the interests between CIES and EVCS via the coordination of flexible demand response and uncertain renewables.