Coordinated energy resource management under uncertainty considering electric vehicles and demand flexibility in distribution systems

Abstract

Distributed energy resources (DER) such as renewable generation and electric vehicles (EVs) are increasingly important to our society, but they impose grid integration challenges that need to be addressed. Wind and photovoltaic generation introduce significant uncertainties as they follow variable weather conditions. A large number of EVs in the grid will raise the level of uncertainty as well. Therefore, to accommodate a large-scale integration of renewables and EVs into the smart grid (SG), models and methods to handle uncertainty and its impact must be adopted. Failing to handle it may lead to unexpected results and high energy costs. The existing formulations for optimal energy resource management (ERM) do not fully consider the problem's uncertainty. In most cases, the uncertainties related to EVs are not considered. With adequate approaches, EVs can be used to support grid imbalance instead of posing a new threat. CENERGETIC - Coordinated ENErgy Resource manaGEment under uncerTainty considering electric vehiCles and demand flexibility in distribution networks, proposed by ISEP in collaboration with UNESP (Brazil), focuses on providing an effective decision support system to manage high levels of DERs, including a large number of EVs, in a coordinated manner. The team already achieved significant results by proposing new approaches for day-ahead ERM including optimal coordination of EVs. CENERGETIC will advance the current state of the art by approaching ERM in different time horizons with uncertainty; proposing new flexibility programs and thus enabling better use of the DERs in a competitive environment. CENERGETIC envisions the existence of several players (e.g., retailers, aggregators, virtual power plants) sharing common resources (e.g. electricity grid), with diverse roles and mutual interrelationships ruled by contractual links. The ERM models will include DSO constraints, flexibility of resources, and consider uncertainty sources (i.e., renewables, EVs, load, market prices) in multi-horizon (i.e., day-ahead, intra­day and real-time). To solve the complexity of the ERM, computational methods (e.g., metaheuristics and exact methods) will be conceived, developed and implemented to tackle the burden of stochastic formulation solving more realistic scenarios, i.e., of high dimension and considering nonlinear constraints in acceptable execution time, which is crucial for the short-term horizon decision making. Finally, a comprehensive decision support system for optimal selection of optimization approach depending on problem complexity will be developed using learning approaches. The proposed models will represent a significant leap enabling the adequate coordination between the distribution system operator (DSO) and the involved players. Overall, CENERGETIC will capture the interdependencies of DERs and ultimately achieve optimal operation with higher integration of DER, thus leading to more economical, environmental and social benefits. (AU)