Stochastic Optimization of Economic Dispatch for Microgrid Based on Approximate Dynamic Programming

Abstract

This paper proposes an approximate dynamic programming (ADP) based approach for the economic dispatch (ED) of microgrid with distributed generations (DGs). The timevariant renewable generation, electricity price and the power demand are considered as stochastic variables in this work. An ADP based ED (ADPED) algorithm is proposed to optimally operate the microgrid under these uncertainties. To deal with the uncertainties, Monte Carlo (MC) method is adopted to sample the training scenarios to give empirical knowledge to ADPED. The piecewise linear function (PLF) approximation with improved slope updating strategy is employed for the proposed method. With sufficient information extracted from these scenarios and embedded in the PLF function, the proposed ADPED algorithm can not only be used in day-ahead scheduling but also the intraday optimization process. The algorithm can make full use of historical prediction error distribution to reduce the influence of inaccurate forecast on the system operation. Numerical simulations demonstrate the effectiveness of the proposed approach. The near-optimal decision obtained by ADPED is very close to the global optimality. And it can be adaptive to both day-ahead and intra-day operation under uncertainty.

RETRACTED ARTICLE: Multi-objective energy management in microgrids with hybrid energy sources and battery energy storage systems

“…Economic dispatch problem was formulated as a quadratic programming problem in grid connected microgrid [42] with an objective of minimization of cost of grid, DG and battery storage system. Dynamic programming based economic dispatch in grid connected microgrid was presented in [43] for minimization total operation cost. Economic schedule of grid connected microgrid with hybrid energy sources was carried out based on distributed model predictive control algorithm and solved using mixed integer linear programming [44].…”

Retracted: Optimal energy dispatch in microgrids with renewable energy sources and demand response

“…Different optimization techniques have been used to solve the MGEM problem. These techniques includes robust optimization, evolutionary approach, linear programming, nonlinear programming, dynamic programming, stochastic programming, multi‐period imperialist competition, Lyapunov optimization, multi‐objective cross entropy, distributed algorithm, nondominated sorting genetic algorithm (GA), Particle Swarm Optimization (PSO), model predictive control, heuristic approach, fuzzy logic, multistep hierarchical, chance constrained programming, artificial intelligence, tabu search, graph theory, SOC‐based control strategy, MATPOWER, GA, flexible time frame, column and constraint generation algorithm, chaotic group search optimizer, Whale Optimization Algorithm (WOA), water cycle algorithm (WCA), Moth‐Flame Optimizer (MFO), and hybrid Particle Swarm‐Gravitational Search Algorithm (PSO‐GSA) . MGEM problem has been studied in conjunction with demand response (DR) program .…”

Optimal Power Dispatch of Small-Scale Standalone Microgrid Located in Colombian Territory

“…Then, the uncertainty distribution must be introduced into the optimization algorithm. This approach has been done using stochastic programming with high success [43], and future improvements to the proposed MILP solution will be focused on achieving similar results. …”