An Integrated Security-constrained Model-based Dynamic Power Management Approach for Isolated Microgrid Power Systems

Microgrid power systems draw lots of interests in marine, aerospace, and electric vehicle applications and are drawing increased attention for terrestrial applications. These power systems, however, are prone to large frequency and voltage deviations, when disturbances happen. Therefore, an effective power management method is needed to operate microgrids optimally, while satisfying operating and security constraints. In this dissertation, a new Integrated Security-Constrained Power Management (ISCPM) method is presented for isolated microgrid power systems during normal/alert operating states. The new ISCPM method was formulated as a multi-objective optimal control problem, in which set-points of several system control methods are minimally adjusted, subject to operating and security constraints, over a period in the future. To solve the ISCPM multi-objective optimal control problem, an evolutionary algorithm based on the Nondominated Sorting GA II (NSGA-II) was developed, in which the optimization solver is linked to a fast simulation core. A fuzzy membership based method was developed to identify the best compromise solution. The new power management method was implemented on a notional computer model for an all-electric ship. The NSGA-II was developed in MATLAB, by adapting a general purpose GA toolbox, IlliGAL. To conduct transient simulations during the GA iterations, the simulation core of the TSAT Tool of Tools TM software package was used. The best compromise solution identification method was developed in MATLAB. To illustrate how the new ISCPM method works in the notional all-electric ship model, several case studies were presented. Also, to evaluate the performance of the new ISCPM method, extensive studies were conducted. For these studies, a detailed electromagnetic transient model of the system in PSCAD was used. The performance analysis addressed quality of the new method from power system operation and multi-objective optimization perspectives. The results indicated that the new ISCPM method could effectively operate the system in an overall near-optimal condition, in which security and operating constraints are also satisfied. The application of the new power management method is not limited to all-electric shipboard power systems and it has great potential to be extended to other types of isolated microgrid power systems

A Framework for Modeling Cyber-Physical Switching Attacks in Smart Grid

Security issues in cyber-physical systems are of paramount importance due to the often safety- critical nature of its associated applications. A rst step in understanding how to protect such systems requires an understanding of emergent weaknesses, in part, due to the cyber-physical coupling. In this paper, we present a framework that models a class of cyber-physical switching vulnerabilities in smart grid systems. Variable structure system theory is employed to effectively characterize the cyber-physical interaction of the smart grid and demonstrate how existence of the switching vulnerability is dependent on the local structure of the power grid. We identify and demonstrate how through successful cyber intrusion and local knowledge of the grid an opponent can compute and apply a coordinated switching sequence to a circuit breaker to disrupt operation within a short interval of time. We illustrate the utility of the attack approach empirically on the Western Electricity Coordinating Council three-machine, nine-bus system under both model error and partial state information.The open access fee for this work was funded through the Texas A&M University Open Access to Knowledge (OAK) Fund

Modelling of Non-linear CHP Efficiency Curves in Distributed Energy Systems

Distributed energy resources gain an increased importance in commercial and industrial building design. Combined heat and power (CHP) units are considered as one of the key technologies for cost and emission reduction in buildings. In order to make optimal decisionson investment and operation for these technologies, detailed system models are needed. These models are often formulated as linear programming problems to keep computational costs and complexity in a reasonable range. However, CHP systems involve variations of the efficiency for large nameplate capacity ranges and in case of part load operation, which can be even of non-linear nature. Since considering these characteristics would turn the models into non23 linear problems, in most cases only constant efficiencies are assumed. This paper proposes possible solutions to address this issue. For a mixed integer linear programming problem two approaches are formulated using binary and Special-Ordered-Set (SOS) variables. Both suggestions have been implemented into the optimization model DER-CAM to simulate investment decisions of CHP micro-turbines and CHP fuel cells with variable efficiencies. The approaches have further been applied successfully in a case study with four different commercial buildings. Comparison of the results between the standard version and the new approaches indicate that total annual system costs remain almost unchanged. System performance is subject to change and storage technologies become more important. Part load operation has mainly been found important for fuel cell units. The micro-turbine is found almost exclusively in full load, thus rendering the application of the new approaches for this technology unnecessary for the considered unit sizes and building types. The approach using binary variables was the most promising method to model variable efficiencies in terms of computational costs and results. It should especially be considered for specific fuel cell technologies. Further investigation on the impacts of this approach on the prediction of fuel cell and micro-turbine performance is suggested

Modeling of non-linear CHP efficiency curves in distributed energy systems

Distributed energy resources gain an increased importance in commercial and industrial building design. Combined heat and power (CHP) units are considered as one of the key technologies for cost and emission reduction in buildings. In order to make optimal decisionson investment and operation for these technologies, detailed system models are needed. These models are often formulated as linear programming problems to keep computational costs and complexity in a reasonable range. However, CHP systems involve variations of the efficiency for large nameplate capacity ranges and in case of part load operation, which can be even of non-linear nature. Since considering these characteristics would turn the models into non23 linear problems, in most cases only constant efficiencies are assumed. This paper proposes possible solutions to address this issue. For a mixed integer linear programming problem two approaches are formulated using binary and Special-Ordered-Set (SOS) variables. Both suggestions have been implemented into the optimization model DER-CAM to simulate investment decisions of CHP micro-turbines and CHP fuel cells with variable efficiencies. The approaches have further been applied successfully in a case study with four different commercial buildings. Comparison of the results between the standard version and the new approaches indicate that total annual system costs remain almost unchanged. System performance is subject to change and storage technologies become more important. Part load operation has mainly been found important for fuel cell units. The micro-turbine is found almost exclusively in full load, thus rendering the application of the new approaches for this technology unnecessary for the considered unit sizes and building types. The approach using binary variables was the most promising method to model variable efficiencies in terms of computational costs and results. It should especially be considered for specific fuel cell technologies. Further investigation on the impacts of this approach on the prediction of fuel cell and micro-turbine performance is suggested

The impact of short-term stochastic variability in solar irradiance on optimal microgrid design:

This paper proposes a new methodology to capture the impact of fast moving clouds on utility power demand charges observed in microgrids with photovoltaic (PV) arrays, generators, and electrochemical energy storage. It consists of a statistical approach to introduce sub-hourly events in the hourly economic accounting process. The methodology is implemented in the Distributed Energy Resources Customer Adoption Model (DER-CAM), a state of the art mixed integer linear model used to optimally size DER in decentralized energy systems. Results suggest that previous iterations of DER-CAM could undersize battery capacities. The improved model depicts more accurately the economic value of PV as well as the synergistic benefits of pairing PV with storage

The impact of short-term stochastic variability in solar irradiance on optimal microgrid design:

This paper proposes a new methodology to capture the impact of fast moving clouds on utility power demand charges observed in microgrids with photovoltaic (PV) arrays, generators, and electrochemical energy storage. It consists of a statistical approach to introduce sub-hourly events in the hourly economic accounting process. The methodology is implemented in the Distributed Energy Resources Customer Adoption Model (DER-CAM), a state of the art mixed integer linear model used to optimally size DER in decentralized energy systems. Results suggest that previous iterations of DER-CAM could undersize battery capacities. The improved model depicts more accurately the economic value of PV as well as the synergistic benefits of pairing PV with storage

Distributed Energy Systems Integration and Demand Optimization for Autonomous Operations and Electric Grid Transactions:

Distributed power systems in the U.S. and globally are evolving to provide reliable and clean energy to consumers. In California, existing regulations require significant increases in renewable generation, as well as identification of customer-side distributed energy resources (DER) controls, communication technologies, and standards for interconnection with the electric grid systems. As DER deployment expands, customer-side DER control and optimization will be critical for system flexibility and demand response (DR) participation, which improves the economic viability of DER systems. Current DER systems integration and communication challenges include leveraging the existing DER and DR technology and systems infrastructure, and enabling optimized cost, energy and carbon choices for customers to deploy interoperable grid transactions and renewable energy systems at scale. This paper presents a cost-effective solution to these challenges by exploring communication technologies and information models for DER system integration and interoperability. This system uses open standards and optimization models for resource planning based on dynamic-pricing notifications and autonomous operations within various domains of the smart grid energy system. It identifies architectures and customer engagement strategies in dynamic DR pricing transactions to generate feedback information models for load flexibility, load profiles, and participation schedules. The models are tested at a real site in California—Fort Hunter Liggett (FHL). The results for FHL show that the model fits within the existing and new DR business models and networked systems for transactive energy concepts. Integrated energy systems, communication networks, and modeling tools that coordinate supply-side networks and DER will enable electric grid system operators to use DER for grid transactions in an integrated system

A Stochastic Optimal Power Flow for Scheduling Flexible Resources in Microgrids Operation

International audienceMicrogrid operations are challenging due to variability in loads and renewable energy generation. Advanced tools capable of taking uncertainty into account are essential to maximize microgrid benefits when operating microgrid owned DERs. This paper proposes a novel optimization model for day-ahead economic dispatch of flexible resources within a microgrid environment, considering uncertainty of PV and loads.This model is conceived to support the microgrid supervisory control layer, providing a security-constrained day-ahead strategy to operate three types of microgrid flexible resources: PV, electric storage and controllable loads. The work presented in this paper introduces a novelty in microgrid operations by presenting a stochastic version of the day ahead scheduling of microgrid DERs to deal with uncertainties associated with PV, load and temperature while considering microgrid network limits and end-user comfort as optimization constraints. An annual analysis quantifies the benefits of to the microgrid-owner of a stochastic formulation over a deterministic one both in terms of ensuring end-user comfort and decreasing operation costs