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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. 

Existing network planning models for electric vehicle (EV) services usually treat the battery swap and the on-board supercharging as two independent processes. This study makes an early attempt to design an EV charging network where battery swap and supercharging are jointly coordinated. The swap and supercharge processes are characterized by Erlang B and Erlang C priority queues, respectively. A strategic location-allocation model is formulated to optimize the station sites, battery stock level, and the number of superchargers at chosen sites. Three design criteria, namely, battery state-of-charge, maximum service time, and power grid constraint, are simultaneously taken into account. Meta-heuristics algorithms incorporating Tabu search are developed to tackle the proposed non-linear mixed integer optimization model. Computational results on randomly generated instances show that the priority battery service scheme outperforms the pure battery swap station in terms of spare battery investment cost and charging flexibility. The case study on a real-world traffic network comprised of 0.714 million households further shows the efficacy and advantage of the dual battery charging process for ensuring state-of-charge, service time commitment, and network-wide grid stability. 

Existing network planning models for electric vehicle (EV) services usually treat the battery swap and the on-board supercharging as two independent processes. This study makes an early attempt to design an EV charging network where battery swap and supercharging are jointly coordinated. The swap and supercharge processes are characterized by Erlang B and Erlang C priority queues, respectively. A strategic location-allocation model is formulated to optimize the station sites, battery stock level, and the number of superchargers at chosen sites. Three design criteria, namely, battery state-of-charge, maximum service time, and power grid constraint, are simultaneously taken into account. Meta-heuristics algorithms incorporating Tabu search are developed to tackle the proposed non-linear mixed integer optimization model. Computational results on randomly generated instances show that the priority battery service scheme outperforms the pure battery swap station in terms of spare battery investment cost and charging flexibility. The case study on a real-world traffic network comprised of 0.714 million households further shows the efficacy and advantage of the dual battery charging process for ensuring state-of-charge, service time commitment, and network-wide grid stability. 

This paper addresses a critical question pertaining to manufacturing sustainability: is it economically viable to implement an island microgrid to power a flow shop system under power demand and supply uncertainty? Though many studies on microgrid sizing are available, the majority assume the microgrid is interconnected with main grid. This paper aims to size wind turbine, photovoltaic and battery storage to energize a multi-stage flow shop system in island mode. A mixed-integer, non-linear programming model is formulated to optimize the renewable portfolio and capacity with the goal of minimizing the levelized cost of energy. The island microgrid is tested in three locations with diverse climate profiles. The results show that net zero energy flow shop production is economically feasible in the areas where the average wind speed exceed 8 m/s at 80-meter tower height, or the battery cost drops below $100,000/MWh. Sensitivity analyses are further carried out with respect to installation cost, demand response program, production scalability, and weather seasonality. 

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. 

This paper investigates the optimal design for a distributed generation (DG) system adopting wind turbines. The paper contribution is to formulate and solve a non-linear stochastic programming model to minimize the system lifecycle cost considering the loss-of-load probability and the thermal constraints using climate data from real settings. The model is solved in three cities representing high to medium to low wind speed profiles. Data analytics on 9-years hourly wind speed records permits to estimate the probability distribution for the power generation. The model is tested in a 9-node DG system with random loads. For a total mean load of 50.1 MW, New York requires the largest number of turbines at the highest annual cost of USD3,071,149, then Rio Gallegos is USD2,689,590, and Wellington is lowest with USD2,509,897. If the total load increases by 6 percent, the system is still capable to meet the reliability criteria but installed wind capacity and annual costs in New York and Rio Gallegos end higher than in Wellington. Results from decreasing the loss-of-load probability from 0.1 to 0.01 percentage show that the system designed using stochastic programming can be highly reliable. 

Power grids based on traditional N-1 design criteria are no longer adequate because these designs do not withstand extreme weather events or cascading failures. Microgrid system has the capability of enhancing grid resilience through defensive or islanded operations in contingency. This paper presents a probabilistic framework for planning resilient distribution system via distributed wind and solar integration. We first define three aspects of resilient distribution system, namely prevention, survivability and recovery. Then we review the distributed generation planning models that comprehend moment estimation, chance constraints and bi-directional power flow. We strive to achieve two objectives: 1) enhancing the grid survivability when distribution lines are damaged or disconnected in the aftermath of disaster attack; and 2) accelerating the recovery of damaged assets through pro-active maintenance and repair services. A simple 9-node network is provided to demonstrate the application of the proposed resilience planning framework