A stochastic model for energy resources management considering demand response in smart grids

Abstract

The ever-increasing penetration level of renewable energy and electric vehicles may threaten power grid operation. Dealing with uncertainty in smart grids is critical in order to mitigate possible issues. This research work proposes a two-stage stochastic model for large-scale energy resources scheduling for aggregators. The proposed model is designed for aggregators managing a smart grid. The idea is to address the challenge brought by the variability of demand, renewable energy, electric vehicles, and market price variations while pursuing cost minimization. Benders' decomposition approach is implemented to improve the tractability of the original model and its' computational burden. A realistic case study is presented using a real distribution network in Portugal with high penetration of renewable energy and electric vehicles. The results show the effectiveness and efficiency of the proposed approach when compared with a deterministic formulation and suggest that demand response and storage systems can mitigate the uncertainty.

Decentralized Electric Vehicle Charging Strategies for Reduced Load Variation and Guaranteed Charge Completion in Regional Distribution Grids

“…It follows that the expected charging load in the lth phase is max 0, bT − p base (l) (18) where p base (l) is the total conventional loads in the lth phase. The charge demand of the ith EV in the lth phase is…”

“…Commonly used algorithms in centralized control, including linear programming, quadratic programming, dynamic programming, stochastic programing, robust optimization, model predictive control, etc., are summarized and presented in [16,17]. A new stochastic model with several uncertainty sources is proposed in [18] to minimize the expected operational cost of the energy aggregator based on stochastic programming, and this method needs a central control center to communicate with the local controllers of DERs, and is required to allow the broadcast of the electricity market prices for the next 24 h. 2.…”

Survey on Complex Optimization and Simulation for the New Power Systems Paradigm

“…Table 2 identifies the characteristics of the main publications we selected in what regards the considered energy resources and in terms of uncertainty consideration in the model. As can be observed in Table 2, very few works attempt to consider most sources of uncertainty in a joint energy scheduling model [45,46]. Moreover, it is yet not common to see works that incorporate vehicle-to-grid (V2G), DG, DR, and energy storage systems (ESS) simultaneously as in [45][46][47][48].…”

“…As can be observed in Table 2, very few works attempt to consider most sources of uncertainty in a joint energy scheduling model [45,46]. Moreover, it is yet not common to see works that incorporate vehicle-to-grid (V2G), DG, DR, and energy storage systems (ESS) simultaneously as in [45][46][47][48]. However, [47,48] do not include consideration of uncertainty of the energy resources.…”

A Hybrid Genetic Wind Driven Heuristic Optimization Algorithm for Demand Side Management in Smart Grid

“…A DR technique formulates the two-stage stochastic problem for energy resource scheduling; inciting the challenges of the renewable sources, electric vehicle and market price uncertainty. It reduces the overall operational cost of the energy aggregator by using stochastic programming [38]. In [39], global load balancing schemes describe the data center power management for minimizing the total electricity cost.…”