IGDT-Based Robust Operation of Thermal and Electricity Energy-Based Microgrid with Distributed Sources, Storages, and Responsive Loads

Abstract

In this paper, the optimal operation of microgrids (MGs) with thermal blocks, distributed generations (DGs), storage systems, and responsive loads is presented to achieve optimal scheduling of active, reactive, and thermal power of the mentioned elements in the day-ahead (DA) reactive power and energy market environment. The thermal block has a combined heat and power (CHP) system, a boiler, and thermally responsive loads. This scheme minimizes the difference between the total operating costs of the MG and power sources and the total revenue gained from the sale of energy and reactive power of the mentioned elements in the markets located in the MG. It is constrained by the AC power flow equations, network operation constraints, and the operating model of these elements. Furthermore, this scheme is subject to the uncertainties of energy price, load, and renewable power. In this paper, to access the optimal resistant solution against the maximum prediction error associated with the mentioned uncertainties, a robust model based on information gap decision theory (IGDT) is used. Finally, by implementing the proposed scheme on a 119-bus radial MG, the obtained numerical results confirm the ability of the scheme to simultaneously improve the economic and operational situation of the MG. The proposed scheme succeeded in improving energy cost, energy loss, voltage drop, and power factor of the distribution substation by roughly 101%, 44%, 41%, and 16% compared to power flow studies, even in the worst-case scenario caused by uncertainties.

Information Gap Decision Theory-Based Risk-Averse Scheduling of a Combined Heat and Power Hybrid Energy System

“…For an islanded CHP-based MG, the IGDT method is used to handle the uncertainty of load and renewable wind power to reduce the cost of operation. Based on the robust scheduling of the IGDT, the authors of [18] proposed an optimization framework to schedule a CHP-based MG. The uncertainty related to renewable generation is handled by the IGDT with a risk-averse strategy.…”

“…Deterministic optimal scheduling of the HES The deterministic optimization presented in this subsection corresponds to a risk-neutral decision-making problem. Similar to any optimization problem, the HES scheduling problem consists of an objective function stated in (3), subject to operational constraints in ( 4)- (18). Equation (3) defines the objective function trying to minimize the total cost.…”

“…C b is the base cost of the system according to forecasted amounts of uncertain parameters. The base cost is the optimal cost of the deterministic problem presented in (3)- (18). ε is the cost deviation factor, which can adjust the target for the robust problem.…”