A Framework of Integrating Manufacturing Plants in Smart Grid Operation: Manufacturing Flexible Load Identification

In the deregulated electricity markets run by Independent System Operator (ISO), a two-settlement (day-ahead and real-time) process is typically used to determine the electricity price to the end-use customers at different buses. In the day-ahead settlement, the demand is predicted at each bus based on the previous consumption behavior of the consumers and thus, Locational Marginal Price (LMP) can be determined and shared to the consumers. A significant gap is usually observed between the planned and real-time demands due to the uncertainties of the weather (temperature, wind-speed etc.), the intensity of business, and everyday activities. Therefore, a large price variation may occur in the real-time market and the dispatching plan needs to be adjusted to respond to the variation. To reduce the gap between the day-ahead and real-time dispatching plans, a modified framework, i.e., a three-settlement process considering the integration of the manufacturing plants into the existing two-settlement process is proposed in this study. The manufacturing end-use customers report the flexibility of their loads to the ISO so that the ISO can update the day-ahead price through an updated dispatching plan that utilizes the feedback of the load flexibility from the manufacturers. A mathematical model is developed to identify the flexible and non-flexible loads of the manufacturers. Particle Swarm Optimization (PSO) is used to solve this mathematical model and a case study is conducted to illustrate the effectiveness of the model

Optimal Onsite Microgrid Design for Net-Zero Energy Operation in Manufacturing Industry

Developing an economic net-zero energy infrastructure for the manufacturing industry can play a critical role to achieve the goal of affordable, reliable, and sustainable clean energy paradigm for the next generation. However, it is quite challenging to develop such an infrastructure due to the uncertain demand of the manufacturing system, intermittent electricity generation from the renewable sources, time of use (TOU) pricing of electricity, and integrated operational planning for the long-term planning horizon. In this paper, a mixed-integer non-linear programming (MINLP) model is developed to economically design an onsite microgrid system considering the critical conditions and achieve a net-zero energy operation planning for the manufacturing industry. A linearization strategy is adopted to obtain the optimal design of the microgrid and the utilization of the resources. A numerical case study is conducted to evaluate the effectiveness of the model

Optimal Scheduling of Manufacturing and Onsite Generation Systems in Over-Generation Mitigation Oriented Electricity Demand Response Program

Manufacturing system is considered a valuable source that can provide electricity load adjustment in electricity demand response program to balance the supply and demand of the electricity throughout the grid. In this paper, we propose a mathematical model to identify the optimal participation strategy for manufacturing end use customers with onsite energy generation system in the demand response program designed for mitigating electricity over-generation due to high penetration of renewable sources in electricity grid. The background of over-generation mitigation oriented demand response program is described first. Then, the manufacturer\u27s decision making procedure for identifying the optimal participation strategy is modeled as a mixed nonlinear integer programming. In particular, the manufacturers€™ participation strategies including the decision of participating or not, and corresponding production schedule of manufacturing system as well as utilization schedule of onsite generation system, are modeled as decision variables in the objective function to minimize the overall cost considering the benefits due to the participation, energy billing cost, onsite generation cost, and production loss penalty cost. Particle swarm optimization is used to find a near optimal solution for the formulated problem. A numerical case study with sensitivity analysis is then conducted to demonstrate the effectiveness and robustness of the proposed model

Routing algorithm for the ground team in transmission line inspection using unmanned aerial vehicle

With the rapid development of robotics technology, robots are increasingly used to conduct various tasks by utility companies. An unmanned aerial vehicle (UAV) is an efficient robot that can be used to inspect high-voltage transmission lines. UAVs need to stay within a data transmission range from the ground station and periodically land to replace the battery in order to ensure that the power system can support its operation. A routing algorithm must be used in order to guide the motion and deployment of the ground station while using UAV in transmission line inspection. Most existing routing algorithms are dedicated to pathfinding for a single object that needs to travel from a given start point to end point and cannot be directly used for guiding the ground station deployment and motion since multiple objects (i.e., the UAV and the ground team) whose motions and locations need to be coordinated are involved. In this thesis, we intend to explore the routing algorithm that can be used by utility companies to effectively utilize UAVs in transmission line inspection. Both heuristic and analytical algorithms are proposed to guide the deployment of the ground station and the landing point for UAV power system change. A case study was conducted to validate the effectiveness of the proposed routing algorithm and examine the performance and cost-effectiveness --Abstract, page iii