Smart microgrids and virtual power plants in a hierarchical control structure

In order to achieve a coordinated integration of distributed energy resources in the electrical network, an aggregation of these resources is required. Microgrids and virtual power plants (VPPs) address this issue. Opposed to VPPs, microgrids have the functionality of islanding, for which specific control strategies have been developed. These control strategies are classified under the primary control strategies. Microgrid secondary control deals with other aspects such as resource allocation, economic optimization and voltage profile improvements. When focussing on the control-aspects of DER, VPP coordination is similar with the microgrid secondary control strategy, and thus, operates at a slower time frame as compared to the primary control and can take full advantage of the available communication provided by the overlaying smart grid. Therefore, the feasibility of the microgrid secondary control for application in VPPs is discussed in this paper. A hierarchical control structure is presented in which, firstly, smart microgrids deal with local issues in a primary and secondary control. Secondly, these microgrids are aggregated in a VPP that enables the tertiary control, forming the link with the electricity markets and dealing with issues on a larger scale

A review of microgrid development in the United States – A decade of progress on policies, demonstrations, controls, and software tools

Microgrids have become increasingly popular in the United States. Supported by favorable federal and local policies, microgrid projects can provide greater energy stability and resilience within a project site or community. This paper reviews major federal, state, and utility-level policies driving microgrid development in the United States. Representative U.S. demonstration projects are selected and their technical characteristics and non-technical features are introduced. The paper discusses trends in the technology development of microgrid systems as well as microgrid control methods and interactions within the electricity market. Software tools for microgrid design, planning, and performance analysis are illustrated with each tool's core capability. Finally, the paper summarizes the successes and lessons learned during the recent expansion of the U.S. microgrid industry that may serve as a reference for other countries developing their own microgrid industries

Compositional Set Invariance in Network Systems with Assume-Guarantee Contracts

This paper presents an assume-guarantee reasoning approach to the computation of robust invariant sets for network systems. Parameterized signal temporal logic (pSTL) is used to formally describe the behaviors of the subsystems, which we use as the template for the contract. We show that set invariance can be proved with a valid assume-guarantee contract by reasoning about individual subsystems. If a valid assume-guarantee contract with monotonic pSTL template is known, it can be further refined by value iteration. When such a contract is not known, an epigraph method is proposed to solve for a contract that is valid, ---an approach that has linear complexity for a sparse network. A microgrid example is used to demonstrate the proposed method. The simulation result shows that together with control barrier functions, the states of all the subsystems can be bounded inside the individual robust invariant sets.Comment: Submitted to 2019 American Control Conferenc

Benchmarking and optimisation of Simulink code using Real-Time Workshop and Embedded Coder for inverter and microgrid control applications

When creating software for a new power systems control or protection device, the use of auto-generated C code via MATLAB Simulink Real-Time Workshop and Embedded Coder toolboxes can be a sensible alternative to hand written C code. This approach offers the benefits of a simulation environment, platform independence and robust code. This paper briefly summarises recent experiences with this coding process including the pros and cons of such an approach. Extensive benchmarking activities are presented, together with descriptions of simple (but non-obvious) optimisations made as a result of the benchmarking. Examples include replacement of certain Simulink blocks with seemingly more complex blocks which execute faster. "S functions" are also designed for certain key algorithms. These must be fully "in-lined" to obtain the best speed performance. Together, these optimisations can lead to an increase in execution speed of more than 1.4x in a large piece of auto-generated C code. An example is presented, which carries out Fourier analysis of 3 signals at a common (variable) frequency. The overall speed improvement relative to the baseline is 2.3x, of which more than 1.4x is due to non-obvious improvements resulting from benchmarking activities. Such execution speed improvements allow higher frame rates or larger algorithms within inverters, drives, protection and control applications