Robust Energy Management for Microgrids With High-Penetration Renewables

Abstract

Abstract-Due to its reduced communication overhead and robustness to failures, distributed energy management is of paramount importance in smart grids, especially in microgrids, which feature distributed generation (DG) and distributed storage (DS). Distributed economic dispatch for a microgrid with high renewable energy penetration and demand-side management operating in grid-connected mode is considered in this paper. To address the intrinsically stochastic availability of renewable energy sources (RES), a novel power scheduling approach is introduced. The approach involves the actual renewable energy as well as the energy traded with the main grid, so that the supplydemand balance is maintained. The optimal scheduling strategy minimizes the microgrid net cost, which includes DG and DS costs, utility of dispatchable loads, and worst-case transaction cost stemming from the uncertainty in RES. Leveraging the dual decomposition, the optimization problem formulated is solved in a distributed fashion by the local controllers of DG, DS, and dispatchable loads. Numerical results are reported to corroborate the effectiveness of the novel approach.Index Terms-Demand side management, distributed algorithms, distributed energy resources, economic dispatch, energy management, microgrids, renewable energy, robust optimization. NOMENCLATURE A. Indices, numbers, and setsT , t Number of scheduling periods, period index. M , m Number of conventional distributed generation (DG) units, and their index. N , n Number of dispatchable (class-1) loads, load index. Q, q Number of energy (class-2) loads, load index. J, jNumber of distributed storage (DS) units, and their index. I, iNumber of power production facilities with renewable energy source (RES), and facility index. S, sNumber of sub-horizons, and sub-horizon index. Parameter of utility function of load q.Depth of discharge specification of DS unit j; and parameters of storage cost. C. Uncertain quantities W t iPower output from RES facility i in period t. D. Decision variables P t GmPower output of DG unit m in period t.Power consumption of load n in period t. P t EqPower consumption of load q in period t. P Power production from all RES facilities in t yielding the worst-case transaction cost. E. Functions C t m (·)Cost of conventional DG unit m in period t. U

Integrated Energy Exchange Scheduling for Multimicrogrid System With Electric Vehicles

“…Among these strategies, genetic algorithm [10] and lagrangian relaxation [11] are proposed to solve the problem of dispatchable generator's output scheduling and the battery charging planning with distributed renewable energy sources from a perspective of centralized control. To avoid the problem that the autonomous behavior of each consumer and the interaction between the consumers and the generators are always ignored in centralized control strategies, game theories [12][13][14] and pricing mechanisms [15,16] are exploited to facilitate the microgrid operation.…”

“…Many strategies are proposed to reduce the total cost or minimize the power losses within a microgrid [10][11][12][13][14][15][16][17]. Most of them focused on individual microgrid optimization without considering the possible impact on the main grid.…”

Asynchronous consensus for optimal power flow control in smart grid with zero power mismatch

“…Hierarchical solutions, where a central coordinator takes on a leadership role, can overcome these problems by forming a multi-agent system (MAS). For example parallel optimization is performed by agents in [4][5][6], while global information updates such as Lagrange multipliers and aggregated load profiles are updated by a central coordinator. And in [7][8][9][10] global information is aggregated by a central entity and then communicated to agents who then apply a game theoretic approach to solving the optimization.…”

Optimal Scheduling of Microgrid with Multiple Distributed Resources Using Interval Optimization

“…In [29], the authors present a robust optimization method to deal with the uncertainty of wind power output and provide a robust unit commitment schedule in the day-ahead market. The authors in [30] propose a robust distributed economic dispatch approach for a grid-connected microgrid with high-penetration renewable energy and optimize the worst-case transaction cost stemming from the uncertainties of the renewable energy. In [31], the authors present a multi-level robust optimization model for an energy-intensive corporate microgrid to minimize the unbalance cost in the worst case considering the uncertainties of loads.…”