Day-ahead Reserve Market Research Articles

Trilevel Mixed Integer Optimization for Day-Ahead Spinning Reserve Management of Electric Vehicle Aggregator With Uncertainty

This paper studies a trilevel profit maximization problem of electric vehicle (EV) aggregator participating in the day-ahead reserve market, considering the uncertain EV connectivity to the grid. At the upper level (UL), the aggregator purchases reserve from individual EVs and trades it in the reserve market. It determines the optimal reserve purchasing price and reserve trading amount to maximize profit. Responding to the reserve purchasing price, at the middle level (ML), each EV owner maximizes his/her own utility by scheduling the battery usage for charging/discharging, reserve or transportation. As the connectivity of EV to the grid is uncertain due to transportation randomness, we characterize the worst-case connectivity at the lower level (LL) such that energy consumption for transportation tasks can be guaranteed. The proposed trilevel optimization problem is challenging because of its multi-level structure and binary variables at ML and LL. Firstly, total unimodularity property, primal-dual and value-function methods are used to convert this problem into a single-level mixed integer nonlinear program (MINLP). Then, a sample-based algorithm is developed to solve the single-level MINLP and the convergence is proved. In addition, an acceleration strategy is proposed to facilitate the computation. Case studies validate the effectiveness of our proposed solution method.

Optimal Reserve Management of Electric Vehicle Aggregator: Discrete Bilevel Optimization Model and Exact Algorithm

This paper investigates the day-ahead optimal reserve management problem of electric vehicle (EV) aggregator. Geographically dispersed EVs are coordinated by the aggregator to participate in the day-ahead reserve market. A bilevel model is proposed to formulate the interaction between the aggregator and the EV owners. In the upper level, the EV aggregator aggregates the reserve capacity provided by the EV owners and then bids in the reserve market. In the lower level, the EV owners decide their optimal energy charging/discharging and reserve capacity based on the reserve price released by the aggregator. Compared with existing works, our proposed bilevel model is more practical. To be specific, we take into account the exclusive right constraint for accessing EV battery, i.e., the battery cannot be simultaneously accessed by the EV owner and the aggregator. This practical model leads to a bilevel mixed integer nonlinear program, which is difficult to solve because the lower level problem incorporates nonconvex integer variables. A novel exact algorithm is developed to solve it and the finite convergence is proved. Comprehensive case studies demonstrate the economic merits of our proposed model to both the aggregator and the EV owners. We also compare the solution optimality with the state-of-the-art approach and thus validate the effectiveness of our proposed exact algorithm.

Enhanced Coordination Strategy for an Aggregator of Distributed Energy Resources Participating in the Day-Ahead Reserve Market

The integration of distributed energy resources (DERs), e.g., electric vehicles (EVs) and renewable distributed generation (DG), in the electrical distribution system (EDS) brings advantages to society, but also introduces technical challenges (e.g., overloading and voltage issues). A DER aggregator, which has agreements with DERs to manage their consumption/generation, could collaborate with the EDS operator to mitigate those technical challenges. Previous approaches have mainly focused on the aggregator’s strategy to manage demand, aiming at the maximization of profits. Therefore, methods to support the aggregator’s strategy need to be extended to facilitate the integration of renewable DG, leading to an enhanced coordination of DERs. This paper proposes a linear programming model for the aggregator’s coordination strategy to maximize its profit through the management of DERs and the participation in the day-ahead reserve market. The model uses EV charging control to provide up/down reserve and reduces its cost taking advantage of DG. The proposed mathematical model represents the daily EDS operation (hourly resolution) to enforce voltage and current magnitude constraints. A case study carried out in an unbalanced 34-bus EDS with 660 EVs, demonstrates that the application of the proposed method enhances the DER aggregator’s strategy, leading to better outcomes in both profits and EDS operation.

Tri‐level decision‐making framework for strategic trading of demand response aggregator

This article presents a new decision-making framework for strategic trading of demand response aggregator (DRA). The proposed framework consists of three layers. At the first layer, the wholesale energy market operated by independent system operator (ISO), which seeks to maximise the social welfare in day-ahead (DA) market and to minimise the cost of minimise the cost for maintaining energy balance in real-time (RT) markets. At the second layer, the DRA act as an intermediate operator between ISO and flexible customers. Profit-maximising DRA affects the clearing results of market prices by strategic trading in DA energy market, providing reserve capacity in DA reserve market and offsetting energy deviation in RT. At the third layer, customers seek to optimise the trading strategy between earnings incentive reward from the DRA and considering uncomfortable cost of customers. The tri-layer framework is modelled as two bilevel optimisation models, which capture interaction between different entities. The bilevel model can be transformed to single level linear model through Karush-Kuhn-Tucker conditions and dual theory to solve the couple non-linear problem. The numerical studies using the IEEE 9-bus and 118-bus test systems are presented to illustrate the application of the proposed tri-layer decision-making framework.