Linear and Nonlinear Model Predictive Control for Distributed Energy Resources in Power Grids

Modern electricity grids are increasingly integrating distributed energy resources (DERs), such as solar plants, battery storage, heat pumps, and electric vehicles. These DERs and dynamic electricity pricing introduce significant fluctuations in generation and load, necessitating advanced control strategies to maintain grid stability and economic operation. Previous research has often neglected the impact of dynamic tariffs and tended to control individual DERs separately rather than through coordinated approaches. To bridge this gap, we present eGridLVOpt, a centralized multi-objective model predictive control (MPC) framework with linear and nonlinear forms. The nonlinear MPC incorporates power flow equations as constraints to capture grid physics, increasing operational safety at the trade-off of higher computational cost. It formulates the control problem as a nonlinear program. In contrast, the linear MPC, formulated as a mixed-integer linear program, uses a simplified nodal balance power grid calculation. Both MPC variants formulate a multiobjective optimization problem balancing objectives, such as maximizing economic profit and minimizing power exchanges. ... mehrOur case studies on a low-voltage distribution grid demonstrate that extending prediction horizons can increase economic gains while simultaneously reducing stress on grid components. Also, nonlinear power flow constraints are necessary to consistently maintain transformer loading within its rated capacity, ensuring safe operation without compromising profitability.