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1. Stochastic Optimal Planning of Community-based Microgrid Considering Energy Justice

   Bai, C; Wang, H; Wang, Z; Wang, Y (August 2025, 2025 IEEE PES General Meeting)

   Low-income households (LIH), exposed to the uncertain modern grid, bear greater energy burdens and face inequitable access to reliable power compared to high-income households (HIH). This paper proposes a two-stage stochastic community-based microgrid planning (CMP) framework to boost energy justice within the system. To reduce the negative impact of income levels, a weighted energy cost model for households within the microgrid (MG) is designed. To address the multisource uncertainty during the operation period, a two-stage stochastic framework is developed. Moreover, to assess the proposed method, the unbalanced IEEE 123 node system is employed and modified as an isolated MG. The analysis reveals the proposed model can achieve a risk-averse solution while economic optimality is guaranteed. Additionally, the designed weighted method improves the LIH’s impact rate to 67.95% and decreases the total planning cost by 22.43%. 

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2. An integrated approach to microgrid sizing and flow shop scheduling via climate analytics

   Subramanyam, V.; Jin, T.; Novoa, C. (April 2020, Journal of cleaner production)

   null (Ed.)

   A variety of methods have been proposed to assist the integration of microgrid in flow shop systems with the goal of attaining eco-friendly operations. There is still a lack of integrated planning models in which renewable portfolio, microgrid capacity and production plan are jointly optimized under power demand and generation uncertainty. This paper aims to develop a two-stage, mixed-integer programming model to minimize the levelized cost of energy of a flow shop powered by onsite renewables. The first stage minimizes the annual energy use subject to a job throughput requirement. The second stage aims at sizing wind turbine, solar panels and battery units to meet the hourly electricity needs during a year. Climate analytics are employed to characterize the stochastic wind and solar capacity factor on an hourly basis. The model is tested in four locations with a wide range of climate conditions. Three managerial insights are derived from the numerical experiments. First, time-of-use tariff significantly stimulates the wind penetration in locations with medium or low wind speed. Second, regardless of the climate conditions, large-scale battery storage units are preferred under time-of-use rate but it is not the case under a net metering policy. Third, wind- and solar-based microgrid is scalable and capable of meeting short-term demand variation and long-term load growth with a stable energy cost rate. 

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3. A two-stage stochastic aggregate production planning model with renewable energy prosumers,” in Proceedings of the 2021 Institute of Industrial and Systems Engineers (IISE) Virtual Conference, May 22-25, 2021, pp. 223-228.

   Islam, S.; Novoa, C.; Jin, T. (June 2021, Proceedings of the 2021 Institute of Industrial and Systems Engineers (IISE) Conference)

   Ghate, A.; Krishnaiyer, K.; Paynabar, K. (Ed.)

   This study presents a two-stage stochastic aggregate production planning model to determine the optimal renewable generation capacity, production plan, workforce levels, and machine hours that minimize a production system’s operational cost. The model considers various uncertainties, including demand for final products, machine and labor hours available, and renewable power supply. The goal is to evaluate the feasibility of decarbonizing the manufacturing, transportation, and warehousing operations by adopting onsite wind turbines and solar photovoltaics coupled with battery systems assuming the facilities are energy prosumers. First-stage decisions are the siting and sizing of wind and solar generation, battery capacity, production quantities, hours of labor to keep, hire, or layoff, and regular, overtime, and idle machine hours to allocate over the planning horizon. Second-stage recourse actions include storing products in inventory, subcontracting or backorder, purchasing or selling energy to the main grid, and daily charging or discharging energy in the batteries in response to variable generation. Climate analytics performed in San Francisco and Phoenix permit to derive capacity factors for the renewable energy technologies and test their implementation feasibility. Numerical experiments are presented for three instances: island microgrid without batteries, island microgrid with batteries, and grid-tied microgrid for energy prosumer. Results show favorable levelized costs of energy that are equal to USD48.37/MWh, USD64.91/MWh, and USD36.40/MWh, respectively. The model is relevant to manufacturing companies because it can accelerate the transition towards eco-friendly operations through distributed generation. 

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4. Mitigating Communication Delay Impact on Microgrid Stability Using a Compensator Based on Smith Predictor

   https://doi.org/10.1109/ECCE.2019.8913101  

   Haghighat, Hadi Akbari; Altin, Necmi; Nasiri, Adel (September 2019, IEEE Energy Conversion Conference and Expo)

   In this study, a control approach based on the model predictive control and Smith Predictor is proposed to compensate the effect of communication delay, and keep the stability of the Microgrid (MG) system. A cyber-physical model for the MG system is introduced to define and test the control and communication functions. The MG system consists of two distributed natural gas generators, an energy storage device, a PV system and a wind turbine with constant power. The Smith Predictor based delay compensator approach is applied to the defined MG based system. In addition, the impact of communication delay on MG is simulated and the behavior of the system with and without compensator is compared. The obtained simulation results show that the proposed approach can significantly decrease the impact of communication latency on frequency deviation and the response of the MG system. 

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5. Probabilistic Microgrid Energy Management with Interval Predictions

   https://doi.org/10.3390/en13123116  

   Cheng, Jiayu; Duan, Dongliang; Cheng, Xiang; Yang, Liuqing; Cui, Shuguang (June 2020, Energies)

   null (Ed.)

   In this paper, we consider a probabilistic microgrid dispatch problem where the predictions of the load and the Renewable Energy Source (RES) generation are given in the form of intervals. A hybrid method combining scenario-selected optimization and reserve strategy using the Model Predictive Control (MPC) framework is proposed. Specifically, first of all, an appropriate scenario is selected by the optimizer at each optimization stage, and then the optimal scheduling and reservation of system capacity are determined based on the selected scenario and possible variations in the future as provided by the predictors. In addition, a new reserve strategy is introduced to adaptively maintain system reliability and respond to variations in the hierarchical microgrid control. Simulations are conducted to compare our proposed method with the existing robust method and the deterministic dispatch with perfect information. Results show that our proposed method significantly improves the system efficiency while maintaining system reliability. 

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