Day-Ahead Smart Grid Cooperative Distributed Energy Scheduling With Renewable and Storage Integration

Abstract

Day-ahead scheduling of generation units and storage devices is essential for the economic and efficient operation of a power system. Conventionally, a control center calculates the dispatch schedule by gathering information from all of the devices. However, this centralized control structure makes the system vulnerable to single point of failure and communication failures, and raises privacy concerns. In this paper, a fully distributed algorithm is proposed to find the optimal dispatch schedule for a smart grid with renewable and energy storage integration. The algorithm considers modified DC power flow constraints, branch energy losses, and energy storage charging and discharging efficiencies. In this algorithm, each bus of the system is modeled as an agent. By solely exchanging information with its neighbors, the optimal dispatch schedule of the conventional generators and energy storage can be achieved in an iterative manner. The effectiveness of the algorithm is demonstrated through several representative case studies.

Energy Management in Microgrids: A Combination of Game Theory and Big Data‐Based Wind Power Forecasting

“…Renewable energy generation Stochastic optimization Handling date uncertainties of renewable energy [10][11][12] Robust optimization [14][15][16][17] Wind power forecasting Linear methods Increasing the accuracy of prediction model [19,20] Nonlinear methods [24][25][26][27] Microgrid management Ordinary decision theory Optimizing energy-scheduling strategies [28][29][30] Noncooperative games [33][34][35][36] Cooperative games [37][38][39][40] and robust optimization [9]. On the one hand, stochastic optimization provides an effective framework to optimize statistical objective functions while the uncertain numerical data are assumed to follow a proverbial probability distribution.…”

“…The authors developed a cooperative distributed energy-scheduling algorithm to optimize the energy dispatch problem while considering the integration of renewable generation and energy storage in Ref. [39]. In Ref.…”

“…It can be treated by the grid as a controllable load or generator and can operate in either islanded or grid-connected mode [5]. However, due to the intermittent and fluctuating characteristics of renewable energy sources and limited generation capacity, the large penetration of uncontrolled and uncoordinated renewable generators into the microgrid especially distribution network will cause a high level of volatility and system disturbances.…”

A review of forecasting algorithms and energy management strategies for microgrids

“…The lower control level concentrates on power system modelling and aims to control BESS to track the power reference. An optimal distributed control scheme should be flexible, robust and address the problem with a costeffective way (Kumar Nunna & Srinivasan, 2016;Morstyn, Hredzak, & Agelidis, 2014;Morstyn, Hredzak, Member, & Vassilios, 2016;Xu, Zhang, Hug, Kar, & Li, 2015;Zhang, Rahbari-asr, Duan, & Chow, 2016;Zhao & Ding, 2018).…”

“…In order to protect the privacy of each participant, Zhao & Ding (2018) presented another cooperative optimal solution for multiple battery energy storage system in a microgrid to maintain the supply-demand mismatch under the wind power generation uncertainties. Similarly, Zhang et al (2016) presented an offline cooperative distributed energy scheduling algorithm, which allowed limited information for agent communication and facilitated reconfiguration of the distributed system. Likewise, in (Kumar Nunna & Srinivasan, 2016), a dynamically updated energy management schedule was presented by altering the operation of storage devices and controllable demand response load to overcome the day-ahead forecasting error and system uncertainty.…”