Networked Microgrids with Electric Vehicle Batteries for Improved Resiliency and Maximum Utilization of Distributed Renewable Energy Resources

In today’s distribution grid, issues have emerged due to the increasing energy production from distributed energy resources such as photovoltaic systems. As the distribution grid is not designed for large-scale production that can lead to bidirectional power flow, problems with voltage regulation, power quality, grid instability, and electrical problems in transformers may arise [1]. Today’s power grid is also vulnerable to power outages due to faults in the grid or targeted attacks against the power grid. With the implementation of networked microgrids, one can reach a higher energy supply provision to critical loads during disconnection from the utility grid. This master’s thesis models and studies a networked microgrid with the aim of highlighting how such grids can increase the resilience of the power grid and how to maximize the use of distributed energy resources. The thesis also focuses on how the battery capacity of electric vehicles can be utilized in the power grid. A case study is designed and modeled to examine these questions in Simulink. The case study examines the resiliency of networked microgrids in comparison to nonnetworked microgrids and consists of three interconnected microgrids. The first microgrid houses a hospital, the second accommodates an office building, and the third includes a residential area. All microgrids contain photovoltaic systems for energy production, electric vehicles for energy storage, and varying loads. 40 % of the load demand in the hospital is defined as critical loads and should be prioritized in case of power outages. Three different scenarios have been simulated and compared. In the first scenario representing a normal operation, the microgrids are interconnected to each other as well as the utility grid. In the second scenario, there is a power outage in the utility grid, and all microgrids are disconnected from each other. This results in all the microgrids operating in islanded mode. In the third and final scenario, a power outage in the utility power grid results in networked microgrid operation where the microgrids exchange power among themselves. The main objective is to supply the critical loads in the hospital with continuous power flow. The results indicated that networked microgrid operation enhances resilience, achieves a higher utilization of distributed energy resources, and validates the feasibility of using electric vehicles as energy storage systems compared to islanded mode operation. The duration of limited power supply to the critical loads decreased by 69.2 %. The total time with load shedding was reduced by 12.3 %, and the maximum load shed value was reduced from 88.7 % to 66.6 %. Further, photovoltaic production increased by 143.7 % in networked microgrid operation compared to operation in islanded mode. Utilizing electric vehicles as energy storage can increase flexibility and can yield a high storage capacity during periods of high demand. However, during periods of low demand, such as nighttime, it may provide a small and sometimes insufficient storage capacity. This work can be enhanced by implementing electricity prices and the use of Mixed-Integer Linear Programming to optimize power flow between each microgrid and between the microgrids and the utility grid. However, due to the time limitations of this master’s thesis, these measurements had to be excluded

VPeak: Exploiting Volunteer Energy Resources for Flexible Peak Shaving

Traditionally, utility companies have employed demand response for large loads or deployed centralized energy storage to alleviate the effects of peak demand on the grid. The advent of Internet of Things (IoT) and the proliferation of networked energy devices have opened up new opportunities for coordinated control of smaller residential loads at large scales to achieve similar benefits. In this paper, we present VPeak, an approach that uses residential loads volunteered by their owners for coordinated control by a utility for grid optimizations. Since the use of volunteer resources comes with hard limits on how frequently they can be used by a remote utility, we present machine learning techniques for carefully selecting which days to operate these loads based on expected peak demand. VPeak uses a distributed and heterogeneous pool of volunteer loads to implement flexible peak shaving that can either selectively target hotspots within the distribution network or perform grid-wide peak shaving. Our results show that VPeak is able to shave up to 26% of the total demand when selectively shaving peaks at local hotspots and up to 46.7% of the demand for grid-wide peak shaving

Coordinated Control of Energy Storage in Networked Microgrids under Unpredicted Load Demands

In this paper a nonlinear control design for power balancing in networked microgrids using energy storage devices is presented. Each microgrid is considered to be interfaced to the distribution feeder though a solid-state transformer (SST). The internal duty cycle based controllers of each SST ensures stable regulation of power commands during normal operation. But problem arises when a sudden change in load or generation occurs in any microgrid in a completely unpredicted way in between the time instants at which the SSTs receive their power setpoints. In such a case, the energy storage unit in that microgrid must produce or absorb the deficit power. The challenge lies in designing a suitable regulator for this purpose owing to the nonlinearity of the battery model and its coupling with the nonlinear SST dynamics. We design an input-output linearization based controller, and show that it guarantees closed-loop stability via a cascade connection with the SST model. The design is also extended to the case when multiple SSTs must coordinate their individual storage controllers to assist a given SST whose storage capacity is insufficient to serve the unpredicted load. The design is verified using the IEEE 34-bus distribution system with nine SST-driven microgrids.Comment: 8 pages, 10 figure

Control and Communication Protocols that Enable Smart Building Microgrids

Recent communication, computation, and technology advances coupled with climate change concerns have transformed the near future prospects of electricity transmission, and, more notably, distribution systems and microgrids. Distributed resources (wind and solar generation, combined heat and power) and flexible loads (storage, computing, EV, HVAC) make it imperative to increase investment and improve operational efficiency. Commercial and residential buildings, being the largest energy consumption group among flexible loads in microgrids, have the largest potential and flexibility to provide demand side management. Recent advances in networked systems and the anticipated breakthroughs of the Internet of Things will enable significant advances in demand response capabilities of intelligent load network of power-consuming devices such as HVAC components, water heaters, and buildings. In this paper, a new operating framework, called packetized direct load control (PDLC), is proposed based on the notion of quantization of energy demand. This control protocol is built on top of two communication protocols that carry either complete or binary information regarding the operation status of the appliances. We discuss the optimal demand side operation for both protocols and analytically derive the performance differences between the protocols. We propose an optimal reservation strategy for traditional and renewable energy for the PDLC in both day-ahead and real time markets. In the end we discuss the fundamental trade-off between achieving controllability and endowing flexibility