Multi-Objective Optimal Dispatching for a Grid-Connected Micro-Grid Considering Wind Power Forecasting Probability

Abstract

In recent years, a large number of wind power has been applied in the micro-grid (MG). Influenced by randomness characteristics of wind speed, the uncertainty in the power output of wind turbines imposes some safety and stability problems on the optimal energy management in MG. To address this problem, an expert energy management system (EEMS) considering wind power probability is developed in this study for optimal dispatching of a typical grid-connected MG. The EEMS composes of wind power probabilistic forecasting module, multi-objective optimization module and energy storage system (ESS) module. In the wind power forecasting module, wind power probabilistic forecasting based on complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and Gaussian process regression (GPR) is proposed in this study. To improve the forecasting results, CEEMDAN, an effective signal processing method, is employed to decompose the wind power data, then, the decomposed subseries are utilized as the inputs of GPR for probabilistic forecasting. A two-step solution methodology combining an efficient and effective improved multi-objective bat algorithm (IMOBA) with fuzzy set theory (FST) is put forward to solve the optimal dispatching problems. In the first step, IMOBA is developed to optimize the energy dispatching of EEMS by minimizing both economic cost and pollutant emissions simultaneously, and obtain a well-distributed set of Pareto optimal front (POF), then, FST is employed to identify the best compromise solutions from POF. Six operational scenarios of a typical grid-connected MG based one-POF-one-day and one-POF-one-hour dispatching schemes are constructed to investigate the effectiveness of the proposed strategy and provide more flexibility for decision makers. The results illustrate that EEMS can effectively schedule power generation and energy storage by considering economic cost and pollutant emission objectives simultaneously. INDEX TERMS Wind power probabilistic forecasting, multi-objective optimization, micro-grids, bat algorithm, expert energy management system. ANG LI received the B.Sc. degree from Shanghai University, Shanghai, China, in 2013, where she is currently pursuing the Ph.D. degree in control science and engineering. Her research interests include positioning in wireless networks, algorithm optimization, and neuro-engineering.

Game Theory-Based Bidding Strategy in the Three-Level Optimal Operation of an Aggregated Microgrid in an Oligopoly Market

“…Buying and selling energy with the upstream network and demand response (DR) planning are undoubtedly among the topics affecting the MG structure that have not been examined in this article. In [29], an expert energy management system is presented to address the difficulty given by the unpredictability of wind power. The EEMS consists of modules for probabilistic wind power forecasting, multiobjective optimization, and energy storage systems (ESSs).…”

“…𝑁𝑁 ℎ ℎ=1 (29) In the second level, resources and demands are scheduled based on the objective function and constraints presented in (1) and ( 3)- (18), respectively.…”

Manta Ray Foraging Optimization for the Virtual Inertia Control of Islanded Microgrids Including Renewable Energy Sources

“…On the other hand, in the islanded mode of operation, either load or generation shedding may be performed to maintain power balance [8,9]. In this regard, some studies have addressed the challenges of microgrid operations in terms of grid-connected mode [10,11] and islanded mode operation [12][13][14][15]. According to the previous studies, microgrid control is more demanding in islanded mode than in grid-connected mode, since the frequency and voltage are not supported by the utility grid [16].…”

Multi-vector energy management system including scheduling electrolyser, electric vehicle charging station and other assets in a real scenario

“…From a review of the controllable assets considered in the literature, all of the works in Table 1 include control of the EES (Marzband et al, 2014;Motevasel and Seifi, 2014;Sfikas et al, 2015;Karavas et al, 2015;Aghajani et al, 2015;Alharbi and Raahemifar, 2015;Talari et al, 2015;Nge et al, 2019;Ramli et al, 2019;Sun et al, 2020), whereas others forms of control, such as micro turbine (MT) (Marzband et al, 2014;Motevasel and Seifi, 2014;Aghajani et al, 2015;Talari et al, 2015;Sun et al, 2020) or responsive loads (RLDs) (Marzband et al, 2014), are used in only some of them. In addition, hydrogen tanks (HTs) (Marzband et al, 2014;Karavas et al, 2015), potable water tanks (PWTs) (Karavas et al, 2015), reverse osmosis desalination (ROD) (Karavas et al, 2015) and combined heat and power (CHP) (Ramli et al, 2019) are controllable assets that appear in some of these works.…”

“…In regard to the way that the optimisation problem is solved, a wide variety of solvers have been implemented. Solvers that use linear programming (Sfikas et al, 2015;Karavas et al, 2015), Lagrange multipliers (LM) (Nge et al, 2019), nonlinear programming (Sfikas et al, 2015) and multi-agent systems (Karavas et al, 2015) have been applied, while the remainder have used stochastic methods such as the most valuable player algorithm (MVPA) (Ramli et al, 2019), the improved multi-objective bat algorithm (IMOBA) (Sun et al, 2020) and the gravitational search algorithm (GSA) (Marzband et al, 2014).…”