Robust Optimization based Energy Dispatch in Smart Grids Considering Simultaneously Multiple Uncertainties: Load Demands and Energy Prices

Abstract

Solving the problem of energy dispatch in a heterogeneous complex system is not a trivial task. The problem becomes even more complex considering uncertainties in demands and energy prices. This paper discusses the development of several Economic Model Predictive Control (EMPC) based strategies for solving an energy dispatch problem in a smart micro-grid. The smart grid components are described using control-oriented model approach. Considering uncertainty of load demands and energy prices simultaneously, and using an economic objective function, leads to a non-linear non-convex problem. The technique of using an affine dependent controller is used to convexify the problem. The goal of this research is the development of a controller based on EMPC strategies that tackles both endogenous and exogenous uncertainties, in order to minimize economic costs and guarantee service reliability of the system. The developed strategies have been applied to a hybrid system comprising some photovoltaic (PV) panels, a wind generator, a hydroelectric generator, a diesel generator, and some storage devices interconnected via a DC Bus. Additionally, a comparison between the standard EMPC, and its combination with MPC tracking in single-layer and two-layer approaches was also carried out based on the daily cost of energy production.

Robust Economic Model Predictive Control Based on a Zonotope and Local Feedback Controller for Energy Dispatch in Smart-Grids Considering Demand Uncertainty

“…Electrical smart grids can be viewed as instances of generalized flow-based networks. Basically, every flow-based network consists of some components [2,18,19,26], e.g., flow sources, flow handling and sink elements, nodes, links and storage elements.…”

“…But, the issue of tractability is often of concern in this approach. Optimization techniques such as min-max MPC approaches as discussed in [14][15][16][17][18][19] can better tackle uncertainties in electrical smart grids. However, one of the main disadvantages of min-max approaches is that, they principally concentrate on worst-case scenarios.…”

“…In general, Open-loop frameworks of robust MPC methodologies are termed to be conservative, while Closed-loop predictions are usually based on controllers with high computational complexity. In [18,19], the authors proposed Closed-loop min-max approach to solve the problem of uncertain economic energy dispatch in smart micro-grids. However, the obtained results show that the computational performance was not only complex but also not optimal as desired.…”

Impacts of Energy Storage System on Power System Reliability: A Systematic Review

“…Table 8 illustrates the impacts of ESS on distribution subsystem (HL3). [60] Solves energy dispatch problem in a smart grid consisting of renewable energy sources and ESS Adopts economic model predictive control to optimize economic costs Does not specify utilization of forced outage rate of system components [61] Proposes integration of tactical and operational management of a micro-grid Uses sample-average approximation (SAA) to optimally improve reliability and system profit Does not specify utilization of forced outage rate of system components [62] Presents a cost-benefit analysis in active distribution system Applies single PSO to identify optimal ESS siting and system net profit Does not specify how solar power is modelled [31] Integrates the use of RTTR alongside ESS to increase the availability of ESS and improve reliability Presents a probabilistic approach of a demand and operational constraints of the ESS May extend research to optimally locate RTTR and thus further improve reliability [63] Reliability evaluation of integrated renewable energy with electric vehicle (EV) operating strategy…”

Robust Economic Model Predictive Control for Energy Dispatch in Smart Micro-grids Considering Demand Uncertainty

“…Optimization techniques such as Minimax MPC approaches as reported in [8] and [9] can better tackle uncertainties in complex systems. However, one of the main drawbacks of Minimax is that, they principally consider worst case scenarios.…”