An Electric Vehicle Dispatch Module for Demand-Side Energy Participation

The penetration of the electric vehicle (EV) has increased rapidly in recent years mainly as a consequence of advances in transport technology and power electronics and in response to global pressure to reduce carbon emissions and limit fossil fuel consumption. It is widely acknowledged that inappropriate provision and dispatch of EV charging can lead to negative impacts on power system infrastructure. This paper considers EV requirements and proposes a module which uses owner participation, through mobile phone apps and on-board diagnostics II (OBD-II), for scheduled vehicle charging. A multi-EV reference and single-EV real-time response (MRS2R) online algorithm is proposed to calculate the maximum and minimum adjustable limits of necessary capacity, which forms part of decision-making support in power system dispatch. The proposed EV dispatch module is evaluated in a case study and the influence of the mobile app, EV dispatch trending and commercial impact is explored.

Improved Real-time State-of-Charge Estimation of LiFePO4 Battery Based on a Novel Thermoelectric Model

Li-ion batteries have been widely used in electric vehicles, and battery internal state estimation plays an important role in the battery management system. However, it is technically challenging, in particular, for the estimation of the battery internal temperature and state-ofcharge (SOC), which are two key state variables affecting the battery performance. In this paper, a novel method is proposed for realtime simultaneous estimation of these two internal states, thus leading to a significantly improved battery model for realtime SOC estimation. To achieve this, a simplified battery thermoelectric model is firstly built, which couples a thermal submodel and an electrical submodel. The interactions between the battery thermal and electrical behaviours are captured, thus offering a comprehensive description of the battery thermal and electrical behaviour. To achieve more accurate internal state estimations, the model is trained by the simulation error minimization method, and model parameters are optimized by a hybrid optimization method combining a meta-heuristic algorithm and the least square approach. Further, timevarying model parameters under different heat dissipation conditions are considered, and a joint extended Kalman filter is used to simultaneously estimate both the battery internal states and time-varying model parameters in realtime. Experimental results based on the testing data of LiFePO4 batteries confirm the efficacy of the proposed method.

Review of virtual power plant applications for the power system management and V2G market development

Utilization of renewable energy sources and energy storage systems is increasing with fostering new policies on energy industries. However, the increase of distributed generation hinders the reliability of power systems. In order to stabilize them, a virtual power plant emerges as a novel power grid management system. The VPP has a role to make a participation of different distributed energy resources and energy storage systems. This paper defines core technology of the VPP which are demand response and ancillary service concerning about Korea, America and Europe cases. It also suggests application solutions of the VPP to V2G market for restructuring national power industries in Korea.

Numerical Analysis on a Dual-Loop Waste Heat Recovery System Coupled with an ORC for Vehicle Applications

The internal combustion (IC) engines exploits only about 30% of the chemical energy ejected through combustion, whereas the remaining part is rejected by means of cooling system and exhausted gas. Nowadays, a major global concern is finding sustainable solutions for better fuel economy which in turn results in a decrease of carbon dioxide (CO2) emissions. The Waste Heat Recovery (WHR) is one of the most promising techniques to increase the overall efficiency of a vehicle system, allowing the recovery of the heat rejected by the exhaust and cooling systems. In this context, Organic Rankine Cycles (ORCs) are widely recognized as a potential technology to exploit the heat rejected by engines to produce electricity. The aim of the present paper is to investigate a WHR system, designed to collect both coolant and exhausted gas heats, coupled with an ORC cycle for vehicle applications. In particular, a coolant heat exchanger (CLT) allows the heat exchange between the water coolant and the ORC working fluid, whereas the exhausted gas heat is recovered by using a secondary circuit with diathermic oil. By using an in-house numerical model, a wide range of working conditions and ORC design parameters are investigated. In particular, the analyses are focused on the regenerator location inside the ORC circuits. Five organic fluids, working in both subcritical and supercritical conditions, have been selected in order to detect the most suitable configuration in terms of energy and exergy efficiencies.

Coupling short-term (B2G model) and long-term (g-function) models for ground source heat exchanger simulation in TRNSYS. Application in a real installation

Ground-source heat pump (GSHP) systems represent one of the most promising techniques for heating and cooling in buildings. These systems use the ground as a heat source/sink, allowing a better efficiency thanks to the low variations of the ground temperature along the seasons. The ground-source heat exchanger (GSHE) then becomes a key component for optimizing the overall performance of the system. Moreover, the short-term response related to the dynamic behaviour of the GSHE is a crucial aspect, especially from a regulation criteria perspective in on/off controlled GSHP systems. In this context, a novel numerical GSHE model has been developed at the Instituto de Ingeniería Energética, Universitat Politècnica de València. Based on the decoupling of the short-term and the long-term response of the GSHE, the novel model allows the use of faster and more precise models on both sides. In particular, the short-term model considered is the B2G model, developed and validated in previous research works conducted at the Instituto de Ingeniería Energética. For the long-term, the g-function model was selected, since it is a previously validated and widely used model, and presents some interesting features that are useful for its combination with the B2G model. The aim of the present paper is to describe the procedure of combining these two models in order to obtain a unique complete GSHE model for both short- and long-term simulation. The resulting model is then validated against experimental data from a real GSHP installation.