Time-delay stability switching boundary determination for DC microgrid clusters with the distributed control framework

In a DC microgrid cluster, distributed DC microgrids are integrated to manage diverse and distributed energy resources. Without the reliance on a management center, the distributed control framework is capable of the cluster deployment by only adjacent collaborations. However, the communication among microgrids and the formation of dispatch signals inevitably lead to time delays, which might cause the system disorder and multiple-delay couplings. Considering these unstable effects, the lack of time-delay study challenges the cluster stability and burdens the energy application. The key contributions of this paper are the definition and detection of the time-delay stability switching boundary for the DC microgrid cluster with the distributed control framework, which reveals time delays switching the system stability and proves the delay-induced oscillation. Through the established time-delay model and the proposed method based on the cluster treatment of characteristic roots, the explicit time-delay stability switching boundary is detected in the delay space, which forms a determination flow of five stages: (1) system initialization: according to the cluster parameter values, the established time-delay model is initialized; (2) space transformation: applying the space mapping and the rationalization, the Sylvester resultant is constructed in the spectral delay space; (3) spectral boundary sketch: in uniformly divided blocks, spectral boundaries are found from the resultant; (4) crossing root calculation: with the spectral boundaries, crossing roots are calculated solving the characteristic equation; (5) boundary determination: back-mapping the spectral boundaries with the crossing roots, the overall boundary is presented. Comprehensive case studies are performed to study the time-delay stability switching boundary and to validate the proposed approach. The boundary existence and feature demonstrate the time-delay effect. Furthermore, the classified stable areas are revealed as well as the relevant strategies for the stability enhancement. © 20182015AA050403; U1766210, NSFC, National Natural Science Foundation of China; 51377117, NSFC, National Natural Science Foundation of ChinaNational High-tech R&D Program of China [2015AA050403]; National Natural Science Foundation of China [U1766210, 51377117

Modeling and Analysis of Scheduling Problems Containing Renewable Energy Decisions

With globally increasing energy demands, world citizens are facing one of society\u27s most critical issues: protecting the environment. To reduce the emission of greenhouse gases (GHG), which are by-products of conventional energy resources, people are reducing the consumption of oil, gas, and coal collectively. In the meanwhile, interest in renewable energy resources has grown in recent years. Renewable generators can be installed both on the power grid side and end-use customer side of power systems. Energy management in power systems with multiple microgrids containing renewable energy resources has been a focus of industry and researchers as of late. Further, on-site renewable energy provides great opportunities for manufacturing plants to reduce energy costs when faced with time-varying electricity prices. To efficiently utilize on-site renewable energy generation, production schedules and energy supply decisions need to be coordinated. As renewable energy resources like solar and wind energy typically fluctuate with weather variations, the inherent stochastic nature of renewable energy resources makes the decision making of utilizing renewable generation complex. In this dissertation, we study a power system with one main grid (arbiter) and multiple microgrids (agents). The microgrids (MGs) are equipped to control their local generation and demand in the presence of uncertain renewable generation and heterogeneous energy management settings. We propose an extension to the classical two-stage stochastic programming model to capture these interactions by modeling the arbiter\u27s problem as the first-stage master problem and the agent decision problems as second-stage subproblems. To tackle this problem formulation, we propose a sequential sampling-based optimization algorithm that does not require a priori knowledge of probability distribution functions or selection of samples for renewable generation. The subproblems capture the details of different energy management settings employed at the agent MGs to control heating, ventilation and air conditioning systems; home appliances; industrial production; plug-in electrical vehicles; and storage devices. Computational experiments conducted on the US western interconnect (WECC-240) data set illustrate that the proposed algorithm is scalable and our solutions are statistically verifiable. Our results also show that the proposed framework can be used as a systematic tool to gauge (a) the impact of energy management settings in efficiently utilizing renewable generation and (b) the role of flexible demands in reducing system costs. Next, we present a two-stage, multi-objective stochastic program for flow shops with sequence-dependent setups in order to meet production schedules while managing energy costs. The first stage provides optimal schedules to minimize the total completion time, while the second stage makes energy supply decisions to minimize energy costs under a time-of-use electricity pricing scheme. Power demand for production is met by on-site renewable generation, supply from the main grid, and an energy storage system. An ε-constraint algorithm integrated with an L-shaped method is proposed to analyze the problem. Sets of Pareto optimal solutions are provided for decision-makers and our results show that the energy cost of setup operations is relatively high such that it cannot be ignored. Further, using solar or wind energy can save significant energy costs with solar energy being the more viable option of the two for reducing costs. Finally, we extend the flow shop scheduling problem to a job shop environment under hour-ahead real-time electricity pricing schemes. The objectives of interest are to minimize total weighted completion time and energy costs simultaneously. Besides renewable generation, hour-ahead real-time electricity pricing is another source of uncertainty in this study as electricity prices are released to customers only hours in advance of consumption. A mathematical model is presented and an ε-constraint algorithm is used to tackle the bi-objective problem. Further, to improve computational efficiency and generate solutions in a practically acceptable amount of time, a hybrid multi-objective evolutionary algorithm based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is developed. Five methods are developed to calculate chromosome fitness values. Computational tests show that both mathematical modeling and our proposed algorithm are comparable, while our algorithm produces solutions much quicker. Using a single method (rather than five) to generate schedules can further reduce computational time without significantly degrading solution quality

Secure Control and Operation of Energy Cyber-Physical Systems Through Intelligent Agents

The operation of the smart grid is expected to be heavily reliant on microprocessor-based control. Thus, there is a strong need for interoperability standards to address the heterogeneous nature of the data in the smart grid. In this research, we analyzed in detail the security threats of the Generic Object Oriented Substation Events (GOOSE) and Sampled Measured Values (SMV) protocol mappings of the IEC 61850 data modeling standard, which is the most widely industry-accepted standard for power system automation and control. We found that there is a strong need for security solutions that are capable of defending the grid against cyber-attacks, minimizing the damage in case a cyber-incident occurs, and restoring services within minimal time. To address these risks, we focused on correlating cyber security algorithms with physical characteristics of the power system by developing intelligent agents that use this knowledge as an important second line of defense in detecting malicious activity. This will complement the cyber security methods, including encryption and authentication. Firstly, we developed a physical-model-checking algorithm, which uses artificial neural networks to identify switching-related attacks on power systems based on load flow characteristics. Secondly, the feasibility of using neural network forecasters to detect spoofed sampled values was investigated. We showed that although such forecasters have high spoofed-data-detection accuracy, they are prone to the accumulation of forecasting error. In this research, we proposed an algorithm to detect the accumulation of the forecasting error based on lightweight statistical indicators. The effectiveness of the proposed algorithms was experimentally verified on the Smart Grid testbed at FIU. The test results showed that the proposed techniques have a minimal detection latency, in the range of microseconds. Also, in this research we developed a network-in-the-loop co-simulation platform that seamlessly integrates the components of the smart grid together, especially since they are governed by different regulations and owned by different entities. Power system simulation software, microcontrollers, and a real communication infrastructure were combined together to provide a cohesive smart grid platform. A data-centric communication scheme was selected to provide an interoperability layer between multi-vendor devices, software packages, and to bridge different protocols together