Qifang Chen

A Heuristic Operation Strategy for Commercial Building Microgrids Containing EVs and PV System

Commercial building microgrids will play an important role in the smart energy city. Stochastic and uncoordinated electric vehicle (EV) charging activities, which may cause performance degradations and overloads, have put great stress on the distribution system. In order to improve the self-consumption of PV energy and reduce the impact on the power grid, a heuristic operation strategy for commercial building microgrids is proposed. The strategy is composed of three parts: the model of EV feasible charging region, the mechanism of dynamical event triggering, and the algorithm of real-time power allocation for EVs. Furthermore, in order to lower the cost of computation resource, the strategy is designed to operate without forecasting on photovoltaic output or EV charging demand. A comprehensive result obtained from simulation tests has shown that the proposed strategy has both satisfactory results and high efficiency, which can be utilized in embedded systems for real-time allocation of EV charging rate.

A Charging Strategy for PV-Based Battery Switch Stations Considering Service Availability and Self-Consumption of PV Energy

The photovoltaic (PV)-based battery switch station (BSS) is one of typical integration systems to implement a solar-to-vehicle system. The charging strategy is important for the operation of the PV-based BSS. Generally, instant charging strategy for swapped electric vehicle (EV) batteries can keep the availability of battery-swapping service at a high level. However, it is always accompanied with the possibility of bringing a negative effect on the utilization of PV energy. The contribution of this paper is mainly on a novel charging strategy for the PV-based BSS considering the service availability and self-consumption of the PV energy. First, considering the features of the PV-based BSS, evaluation indexes for the operation performance are defined, including the availability of battery-swapping service, self-consumption of the PV energy, and operation profit. Second, the charging strategy is proposed, including a battery-swapping service model and a power distribution model. In order to guarantee the service availability, the battery-swapping service model is used to decide the lower limit of charging power based on short-term forecasting results of EV requirements. The power distribution model is obliged to dispatch the charging power supplied by the PV system and power grid. Finally, in the case study, the operation of the BSS is simulated with the instant charging strategy and the proposed strategy under different scenarios. From the analysis of results, the proposed strategy can effectively improve the self-consumption of PV energy with the premise of guaranteeing the availability of the battery-swapping service.

Dynamic Price Vector Formation Model-Based Automatic Demand Response Strategy for PV-Assisted EV Charging Stations

A real-time price (RTP)-based automatic demand response (ADR) strategy for PV-assisted electric vehicle (EV) Charging Station (PVCS) without vehicle to grid is proposed. The charging process is modeled as a dynamic linear program instead of the normal day-ahead and real-time regulation strategy, to capture the advantages of both global and real-time optimization. Different from conventional price forecasting algorithms, a dynamic price vector formation model is proposed based on a clustering algorithm to form an RTP vector for a particular day. A dynamic feasible energy demand region (DFEDR) model considering grid voltage profiles is designed to calculate the lower and upper bounds. A deduction method is proposed to deal with the unknown information of future intervals, such as the actual stochastic arrival and departure times of EVs, which make the DFEDR model suitable for global optimization. Finally, both the comparative cases articulate the advantages of the developed methods and the validity in reducing electricity costs, mitigating peak charging demand, and improving PV self-consumption of the proposed strategy are verified through simulation scenarios.

Autonomous Energy Management Strategy for Solid-State Transformer to Integrate PV-Assisted EV Charging Station Participating in Ancillary Service

Photovoltaic-assisted charging station (PVCS) is expected to be one of the important charging facilities for serving electric vehicles (EVs). In this paper, a type of solid-state transformer (SST) is introduced to the PVCS design and an autonomous energy management strategy (EMS) for SST is proposed. This study aims to develop an effective real-time EMS for PVCS participating in ancillary service of smart grid, and the rule-based decision-making method is utilized. Considering the dynamic classification of EVs, an energy-bound calculation (EBC) model is proposed to find the upper and lower bounds of flexible resources. Moreover, considering the EBC results and power command from the aggregator, a charging power allocation algorithm is designed for power distribution of flexible EVs. By case study and experiment analysis, the proposed EMS is effective in real-time energy management and suitable for practical applications.

Optimal power utilizing strategy for PV-based EV charging stations considering Real-time price

Developing EVs has been established as an effective way to reduce carbon emission in development and ensure energy security by many countries. The PV-based EVCS (EV charging station) can efficiently alliviate the charging pressures of the distribution network and reduce EVs indirect emissions. A Real-time power utilizing strategy considering real-time price for PV-based EV charging station is proposed to allow EVs to participate in DR (Demand Response) and charge economically. The DR is adopted for power utilizing strategy to offer a kind of financial benefits for EVs and minimize the charging fees. The behavior mode and the CRFR mode are utilized to figure out the EV demand mode. The PSO (Paticle Swarm Optimal) algorithm is utilized to optimally allocate the charging power. The real-time price is forecasted by wavelet neural network based on the historical data and the total charging power responses to the real-time price so as to minimize charging cost. A case study is simulated to validate the proposed method.

Online Energy Sharing for Nanogrid Clusters: A Lyapunov Optimization Approach

Nanogrid (NG) cluster (NGC) has the potential to act as one type of basic structure for the future low voltage distribution networks. In this paper, an online energy sharing method is proposed for improving the self-sufficiency and photovoltaic (PV) consumption of NGC. First, a hybrid cyber-physical peer-to-peer (P2P) energy sharing framework is proposed, which is a combination of P2P physical system (i.e., NG-to-NG) and client–server cyber system (i.e., NG controllers-central controller). Moreover, an energy sharing strategy with the classification of energy exporting and importing NGs is designed. Considering the stochastic features of PV energy and end user load, an online optimization model and algorithm is formulated based on Lyapunov optimization, as to maximize the self-sufficiency and guarantee the stability of energy storage queues. Finally, in a case study using the realistic data from the residential community, numerical experiments show the effectiveness of the proposed method in improving the self-sufficiency of NGC.

Optimal Configuration for Batteries and Chargers in Battery Switch Station Considering Extra Waiting Time of Electric Vehicles

AbstractBattery switch stations (BSSs) are regarded as important infrastructure for the development of electric vehicles (EVs). However, studies on the optimal configuration of BSSs are still limited. In order to assist the design of BSS, an optimal configuration framework based on operational planning method is proposed. An optimal model is formulated to minimize investment, maintenance, and electricity cost of BSSs. The extra waiting time (EWT) of EVs is taken as a constraint for service quality. A simulation model for operations of a BSS is designed and the states of EVs and batteries under different operation strategies can be simulated so the EWT for each EV can be calculated. Furthermore, differential evolution (DE) is used to solve the optimal configuration model. Finally, the actual data and parameters of electric taxis and electric buses are used for the case study. Results show the quantitative relations among EVs, batteries, and chargers under different levels of average EWT can be obtained. Th...

Real-time energy management algorithm for PV-assisted charging station considering demand response

Photovoltaic (PV)-assisted charging station is one of important charging facilities served for Electric Vehicles (EV). To minimize the operation cost of photovoltaic (PV)-assisted electric vehicle (EV) charging station, an energy management considering demand response (DR) strategy is proposed. The wavelet neural network (WNN) is utilized to forecast the price based on history data and the forecasting result is regarded as the basic price vector. Real-time price is utilized to replace basic price in current time slot to form the new price vector (NPV). The feasible energy demand region (FEDR) model is utilized to calculate the lower bounds and upper bounds dynamically. The dynamic linear programming (DLP) algorithm is utilized to calculate the optimal charging energy schedule based on the NPV and FEDR model. A comprehensive result obtained from comparison simulations has shown that the proposed ADR strategy is excellent in reducing cost, improving PV self-consumption and mitigating charging peak load on grid.