Increasing distributed generation hosting capacity in distribution systems via optimal coordination of electric vehicle aggregators

Abstract

This work presents a novel strategy, designed from the distribution system operator viewpoint, aimed at estimating the hosting capacity in electric distribution systems when controllable plug‐in electric vehicles are in place. The strategy seeks to determine the maximum wind‐based distributed generation penetration by coordinating, on a forecast basis, the dispatch of electric vehicle aggregators, the operation of voltage regulation devices, and the active and reactive distributed generation power injections. Different from previous works, the proposed approach leverages controllable features of electric vehicles taking into account technical electric vehicle characteristics, driving behaviour of electric vehicle owners, and electric vehicle energy requirements to accomplish their primary purpose. The presented strategy is formulated as a two‐stage stochastic mixed‐integer linear programming problem. The first stage maximises the distributed generation installed capacity, while the second stage minimises the energy losses during the planning horizon. Probability density functions are used to describe the uncertainties associated with renewable distributed generation, conventional demand, and electric vehicle driving patterns. Obtained results show that controlling the power dispatched to electric vehicle aggregators can increase the distributed generation hosting capacity by up to 15% (given a 40% electric vehicle penetration), when compared to an uncontrolled electric vehicle approach.

Simultaneous Distributed Generation and Electric Vehicles Hosting Capacity Assessment in Electric Distribution Systems

“…This information is sent to the DSO, and then the DSO determines, for each time interval t, the power drawn by the aggregator in a way that meets the energy requirements of the EVs' and the EDS's operational limits. To model the aggregators and determine the population of EVs that can be connected to them, the expressions ( 7)- (15), based on [25], are used.…”

“…time intervals where there is a surplus of renewable generation. In addition, this operational resource should meet the constraints formulated in (24) and (25), where the curtailment is limited to a percentage that can be defined as a prior agreement between the DSO and DG developers. However, if the maximum generation curtailment availability is reached and the infeasible conditions still remain, then iteration a is ended and the process continues to the next l iteration.…”

“…Later, the strategy in [19] is improved in [20], where a strategy based on a demand response mechanism is implemented to maximize the EV penetration level. On the other hand, by taking advantage of the EV charging coordination, the integration of other resources could be maximized, such as heat pumps [21], and DG [22]- [25].…”

“…In the DG hosting capacity problem, EVs have been scarcely studied; however, an initial approach is presented in [22], which estimates the maximum DG capacity to be installed considering EVs as uncontrollable loads. To overcome this limitation, some studies present different approaches to increase the DG installed capacity, taking advantage of controllable features of the EVs [23]- [25]. Although these works exploit, from the perspective of the DSO, the benefits that controllable features of EVs can provide in the EDS operation, these studies assume that EVs are placed in the system; therefore, the simultaneous hosting capacity assessment of DG and EVs in EDSs is not considered in such approaches.…”

“…Although there is a vast number of approaches that address the DG hosting capacity problem, only [23]- [25] consider the effects of EVs. As discussed in the literature review, approaches to simultaneously estimate the hosting capacity of DG and EVs in EDSs have been scarcely proposed, where only the work presented in [26] is found.…”