The MicroGrid (MG) is the important part of the SmartGrid system and the Energy Management System (EMS) determines the performance of a MicroGrid. This paper presents a generalized formulation to determine the optimal strategy and cost optimization of the EMS for a MicroGrid of the small-modular residential area. The EMS includes the generating capacity of diesel generator, PV array, purchasing or selling electricity, and the state of battery-charging or discharging. This paper proposes the optimization problem of the EMS, which is formulated as a mixed-integer quadratic programming (MIQP) problem. In the EMS, the predictions of the demand and the output of PVs are often inaccurate, and to solve this problem, this paper adopts rolling optimization to implement the EMS algorithm. Meanwhile, the rolling optimization utilizes the multi-point radiation based parallel branch and bound (MPRP-BB) algorithm on GPU to improve the computational efficiency. The simulation results compare the advantages and disadvantages of the rolling optimization and global optimization, which verifies the effectiveness of the proposed method.

Hybrid modeling and optimization for Energy Management System of MicroGrid

Binary quadratic optimization

The Methods for Closing Surrogate Dual GAP in Integer Linear Programs

The control of infrastructure systems, such as the electric power supply, water distribution or traffic networks, is challenging for many reasons. The systems are large-scale, highly complex, subject to constraints and require a safe and cost-efficient operation. An appropriate control method is Model Predictive Control (MPC). Because of its inherent continuous optimization, MPC is known to be a compute- and, especially for large systems, memory-intensive process. Hence, MPC was unfeasible for energy-constrained embedded implementations for a long time. Recently, this issue has been overcome by utilizing highly energy-efficient hardware platforms like Field Programmable Gate Arrays (FPGAs). However, the amount of available memory on an FPGA restricts the achievable system size of MPC implementations. Therefore, the realization of large MPC systems like those requiring long prediction horizons is difficult on FPGAs. This paper proposes structural data compression (SDC), a technique that drastically lowers the memory demand of embedded MPC on FPGAs. We show how SDC can reduce the memory requirements by a factor higher than 80x compared to common code-generated MPC designs, thereby enabling MPC systems that require long prediction horizons to be implemented on an embedded device.

Structural Data Compression for Embedded Long Prediction Horizon Model Predictive Control on Resource-Constrained FPGA Platforms

The on-line robust model predictive control (RMPC) has heavy on-line computation, which limits the application for the fast system. Meanwhile, the robust design may lead to the poor performance for the system with unknown but fixed model. Considering the above situations, this paper proposes an efficient data-driven MPC with input mapping method. The key contribution in this paper is that the online input mapping compensation method directly uses the combination of the past output data and their mapping relation to reduce the conservatism of RMPC. And the linear programming method is used for solving the optimization problem and obtaining the combination coefficients. It is shown that the online input mapping compensation method can improve control performance. Meanwhile, the proposed design is proven to be feasible and stable. The numerical simulation demonstrates the effectiveness of the proposed control method.

The Efficient Data-Driven MPC with Input Mapping Method Based on Polyhedral Invariant Set

Solving the mixed-integer quadratic programming (MIQP) problem is often required in many practical applications. But the existing solvers always encounter the contradiction between high precision and low time consumption. To solve this problem, this paper designs a new MIQP solver by developing a parallel branch-and-bound algorithm utilizing multi-point radiation based on the multithreading parallel structure of GPU. This solver can obtain the global optimal solution by inheriting the advantages of the classical branch-and-bound algorithm. To increase the efficiency of the MIQP solver, for the quadratic programming (QP) to be solved each time during iteration, we fully use the massive parallelism of GPU and adopt the discrete-time simplified dual neural network. The idea of multi-point radiation is used to simultaneously generate multiple search branches to improve the search efficiency. These strategies enhance the throughput and the degree of parallelization. The high computational efficiency of the proposed MIQP solver is verified by test results with solving time statistics for multiple examples.

A mixed-integer quadratic programming solver based on GPU

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A mixed-integer quadratic programming solver based on GPU