Multi-Time-Scale Rolling Optimal Dispatch for AC/DC Hybrid Microgrids With Day-Ahead Distributionally Robust Scheduling

Data-driven adaptive robust optimization for energy systems in ethylene plant under demand uncertainty

In order to reduce the impact of the randomness and volatility of renewable energy on the economic operation of AC/DC hybrid microgrids, a multi-time-scale rolling optimization strategy is proposed for the grid-connected AC/DC hybrid microgrids. It considers the source-load uncertainty declined with time scale reduction, and the scheduling cooperation problem of different units on different time scales. In this paper, we propose a three-time-scale optimal strategy of the day-ahead, intraday and real-time dispatching stage and a two-level rolling optimal strategy of the intraday and real-time stage, aiming at minimizing the operating cost. We added the power penalty cost in the rolling optimization model to limit the energy state of the energy storage system in the constraint, and improve the power correction and tracking effect of the rolling optimization. A typical-structure AC/DC hybrid microgrid is analyzed in this paper and the simulation results are shown to demonstrate the feasibility and effectiveness of the proposed multi-time-scale rolling optimal dispatch.

A robust optimization framework for energy management of CCHP users with integrated demand response in electricity market

To combat the multiple uncertainties of AC/DC hybrid microgrid, the paper presents a two-stage optimal dispatch strategy for AC/DC hybrid microgrid. The strategy is divided into two stages: day-ahead economic dispatch and intraday rolling optimal dispatch. The objective of day ahead stage is to minimize the daily operation cost, which takes into account the operation characteristics of microgrid components and time-of-use electricity price. The intraday rolling optimal dispatch model is carried out based on model predictive control (MPC). Combining the rolling optimization with feedback compensation, the closed-loop optimization is developed. Depending on that, the model can effectively suppress the intraday power fluctuations and avoid affecting the main grid operation. According to the simulation results, the proposed strategy can effectively realize the economic and autonomous operation of AC/DC hybrid microgrid.

<abstract><p>Robust optimization is a new modeling method to study uncertain optimization problems, which is to find a solution with good performance for all implementations of uncertain input. This paper studies the optimal location allocation of processing plants and distribution centers in uncertain supply chain networks under the worst case. Considering the uncertainty of the supply chain and the risk brought by the uncertainty, a data-driven two-stage sparse distributionally robust risk mixed integer optimization model is established. Based on the complexity of the model, a distribution-separation hybrid particle swarm optimization algorithm (DS-HPSO) is proposed to solve the model, so as to obtain the optimal location allocation scheme and the maximum expected return under the worst case. Then, taking the fresh-food supply chain under the COVID-19 as an example, the impact of uncertainty on location allocation is studied. This paper compares the data-driven two-stage sparse distributionally robust risk mixed integer optimization model with the two-stage sparse risk optimization model, and the data results show the robustness of this model. Moreover, this paper also discusses the impact of different risk weight on decision-making. Different decision makers can choose different risk weight and obtain corresponding benefits and optimal decisions. In addition, the DS-HPSO is compared with distribution-separation hybrid genetic algorithm and distributionally robust L-shaped method to verify the effectiveness of the algorithm.</p></abstract>

With the aggravation of population aging, home health care (HHC) services are paid more and more attention by the elderly. Previous studies aim at improving service quality and reducing cost, ignoring the coordinated and sustainable development of the economy and environment. From the perspective of sustainable development, this paper first establishes a linear optimization (LO) model considering transportation, time, and carbon emission costs. However, the uncertainty of service demand is a very difficult problem for HHC research. Most of the previous studies only consider the deterministic model, which has difficulty dealing with the uncertain situation. Therefore, a robust optimization (RO) model is proposed to resist uncertain disturbances by introducing a robust uncertain set response. The experimental results show that the increase of low‐carbon transition cost only increases the total cost of the LO model but has a significant positive impact on the RO model. With the increase of uncertainty, the robust model will pay the cost of robustness, but it can obtain a higher service level (93.20% to 93.38%). In addition, when the carbon tax increases, the total transportation cost does not increase but decreases, thus obtaining environmental benefits. When the carbon tax increases by 25%, the average total cost of using the RO model is reduced by 8.274%. The research results of this paper can provide enlightenment and reference for the low‐carbon transformation of HHC enterprises.

Active power dispatch and reactive power optimization problems are usually handled separately in active distribution systems, aiming at minimizing the total generation cost or transmission losses. However, the separate optimization cannot achieve a global optimum scheme in distribution system operations. Moreover, the significant relationship between the active power and reactive power may pose great challenges to distribution system operations due to the uncertain nature of load demands and intermittent renewable energy resources. In this paper, using the branch flow model-based relaxed optimal power flow, we formulate a robust coordinated optimization problem for active and reactive powers as a mixed integer second-order cone (SOC) programming problem. Furthermore, in order to address the uncertainties, a two-stage robust optimization model is proposed to coordinate the on load tap changer ratios, reactive power compensators, and charge–discharge power of energy storage system to find a robust optimal solution. Then the column-and-constraint generation algorithm is applied to solve the proposed robust two-stage optimization model. In the relaxed optimal power flow, a stricter cut is added to speed up the computation process of the SOC relaxation in order to guarantee the exactness for representative cases, such as those with high penetration of distributed energy resources. Numerical results based on the 33-bus and 69-bus systems verify the effectiveness of the proposed method.

A data-driven distributionally robust optimization model for multi-energy coupled system considering the temporal-spatial correlation and distribution uncertainty of renewable energy sources

An optimized scheduling strategy combining robust optimization and rolling optimization to solve the uncertainty of RES-CCHP MG

Bi-level multi-objective robust optimization for performance improvements in integrated energy system with solar fuel production

This paper proposes a microgrid adaptive robust optimal dispatch model with different robust adjustment parameters to improve the operating economy and safety of large‐scale renewable distributed energy integration into the microgrid system. Through a robust equivalent process, considering the uncertain characteristics of the renewable energy output and load demand in the microgrid, the uncertain parameters are converted into corresponding definite parameters. A two‐stage robust optimization model is established to minimize the operating cost of the microgrid under the premise of ensuring the robustness of the microgrid. An improved Benders algorithm is proposed to solve the established optimization model. The different robust adjustment parameters can be obtained adaptively through the optimization program. The optimized adaptive robust adjustment parameters can better reflect the balance between the economy and robustness of the microgrid operation, and are more suitable for the operation of the microgrid. The improved Benders algorithm can effectively speed up the solution and improve the efficiency of the solution. The simulation of the modified IEEE39‐bus system verifies the rationality of the proposed optimization model and the advantages of the improved algorithm.

Multistage adaptive robust optimization for the hydrothermal scheduling problem

Maritime emergency materials distribution is a key aspect of maritime emergency responses. To effectively deal with the challenges brought by the uncertainty of the maritime transport environment, the multi-agent joint decision-making location-routing problem of maritime emergency materials distribution (MEMD-LRP) under an uncertain decision-making environment is studied. First, two robust bi-level optimization models of MEMD-LRP are constructed based on the effect of the uncertainty of the ship’s sailing time and demand of emergency materials at the accident point, respectively, on the premise of considering the rescue time window and priority of emergency materials distribution. Secondly, with the help of robust optimization theory and duality theory, the robust optimization models are transformed into robust equivalent models that are easy to solve. Finally, a hybrid algorithm based on the ant colony and tabu search (ACO-TS) algorithm solves multiple sets of numerical cases based on the case design of the Bohai Sea area, and analyzes the influence of uncertain parameters on the decision making of MEMD-LRP. The study of MEMD-LRP under uncertain decision-making environments using bi-level programming and robust optimization methods can help decision makers at different levels of the maritime emergency logistics system formulate emergency material reserve locations and emergency material distribution schemes that can effectively deal with the uncertainty in maritime emergencies.

Mars entry trajectory robust optimization based on evidence under epistemic uncertainty

Operation characteristics analysis and optimal dispatch of solar thermal-photovoltaic hybrid microgrid for building