abstract: This dissertation develops advanced controls for distributed energy systems and evaluates performance on technical and economic benefits. Microgrids and thermal systems are of primary focus with applications shown for residential, commercial, and military applications that have differing equipment, rate structures, and objectives. Controls development for residential energy heating and cooling systems implement adaptive precooling strategies and thermal energy storage, with comparisons made of each approach separately and then together with precooling and thermal energy storage. Case studies show on-peak demand and annual energy related expenses can be reduced by up to 75.6% and 23.5%, respectively, for a Building America B10 Benchmark home in Phoenix Arizona, Los Angeles California, and Kona Hawaii. Microgrids for commercial applications follow after with increased complexity. Three control methods are developed and compared including a baseline logic-based control, model predictive control, and model predictive control with ancillary service control algorithms. Case studies show that a microgrid consisting of 326 kW solar PV, 634 kW/ 634 kWh battery, and a 350 kW diesel generator can reduce on-peak demand and annual energy related expenses by 82.2% and 44.1%, respectively. Findings also show that employing a model predictive control algorithm with ancillary services can reduce operating expenses by 23.5% when compared to a logic-based algorithm. Microgrid evaluation continues with an investigation of off-grid operation and resilience for military applications. A statistical model is developed to evaluate the survivability (i.e. probability to meet critical load during an islanding event) to serve critical load out to 7 days of grid outage. Case studies compare the resilience of a generator-only microgrid consisting of 5,250 kW in generators and hybrid microgrid consisting of 2,250 kW generators, 3,450 kW / 13,800 kWh storage, and 16,479 kW solar photovoltaics. Findings show that the hybrid microgrid improves survivability by 10.0% and decreases fuel consumption by 47.8% over a 168-hour islanding event when compared to a generator-only microgrid under nominal conditions. Findings in this dissertation can increase the adoption of reliable, low cost, and low carbon distributed energy systems by improving the operational capabilities and economic benefits to a variety of customers and utilities. / Dissertation/Thesis / Doctoral Dissertation Engineering 2019

abstract: In recent years, many school districts, community colleges, and universities in California have implemented energy management-as-a-service (EMaaS). The purpose of this study was to analyzes how EMaaS has been realized in California schools, including how performance expectations and service guarantees have been met, how value is created and captured, and which trends are emerging in the pay-for-performance models. This study used a qualitative research design to identify patterns in the collected data and allow theories to be drawn from the emergent categories and themes. Ten in-depth interviews were conducted with a diverse pool of facility managers, energy practitioners, superintendents, and associate superintendents working with EMaaS. Four themes emerged (1) peak shaving overperformance, (2) low risk/reward, (3) performance exactly as expected, and (4) hope in future flexibility. This study reveals medium to high levels of performance satisfaction from the customers of cloud-enabled and battery-based EMaaS in California schools. Value has been captured primarily through peak shaving and intelligent bill management. Large campuses with higher peaks are especially good at delivering energy savings, and in some instances without pairing batteries and solar. Where demand response participation is permitted by the utility companies, the quality of demand response performance is mixed, with performance being exactly as expected to slightly less than expected. The EMaaS business model is positioned to help California schools implement and achieve many of their future sustainability goals in a cost-effective way. / Dissertation/Thesis / Masters Thesis Construction Management 2020

The purpose of this study is to analyse how a battery energy storage system (BESS) can support the frequency and voltage stability for an islanded microgrid containing a hydropower plant. Two different microgrids, both situated in Sweden, are evaluated. Modelling and dynamic simulations are conducted in the PowerFactory tool. The result shows that both the frequency and the voltage control can be improved with the BESS. However, with the allowed limit of ± 1 Hz, not all simulated scenarios including a BESS meets the requirement. A large difference between the BESS and generator capacity might be a possible cause for this. By dividing the larger loads so that smaller loads are attained, the frequency deviation might be reduced. Furthermore, by adjusting the systems PID-parameters according to the island mode operation, faster regulation can be attained. The system operates according to the Master slave control strategy, with the hydropower being the master unit with voltage control and the BESS being a slave unit with PQ control. The ability to operate an islanded microgrid can ensure the supply of electricity to inhabitants and vital functions in society. By utilizing a BESS for increasing electric stability, emission of CO2 is indirectly mitigated. As cost for BESS are expected to decrease rapidly, they will be accessible for utilization all over the world.

In the first part of this thesis, we propose a distributed consensus algorithm under multi-layer multi-group structure with communication time delays. It is proven that the consensus will be achieved in both time-varying and fixed communication delays. In the second part, we study the distributed optimization problem with a finite-time mechanism. It is shown that our distributed proportional-integral algorithm can exponentially converge to the unique global minimizer when the gain parameters satisfy the sufficient conditions. Moreover, we equip the proposed algorithm with a decentralized algorithm, which enables an arbitrarily chosen agent to compute the exact global minimizer within a finite number of time steps, using its own states observed over a successive time steps. In the third part, it is shown the implementation of accelerated distributed energy management for microgrids is achieved. The results presented in the thesis are corroborated by simulations or experiments.

<p></p><p>The modern U.S. power grid is susceptible to a variety of vulnerabilities, ranging from aging infrastructure, increasing demand, and unprecedented interactions (e.g., distributed energy resources (DERs) generating energy back to the grid, etc.). In addition, the rapid growth of new technologies such as the Internet of Things (IoT) affords promising new capabilities, but also accompanies a simultaneous risk of cybersecurity deficiencies. Coupled with an electrical network referred to as one of the most complex systems of all time, and an overall D+ rating from the American Society of Civil Engineers (ASCE), these caveats necessitate revaluation of the electrical grid for future sustainability. Several solutions have been proposed, which can operate in varying levels of coordination. A microgrid topology provides a means of enhancing the power grid, but does not fundamentally solve a critical issue surrounding energy consumption at the endpoint of use. This results from the necessary conversion of Alternating Current (AC) power to Direct Current (DC) power in the vast majority of devices and appliances, which leads to a loss in usable energy. This situation is further exacerbated when considering energy production from renewable resources, which naturally output DC power. To transport this energy to the point of application, an initial conversion from DC to AC is necessary (resulting in loss), followed by another conversion back to DC from AC (resulting in loss).</p> <p> </p> <p>Tackling these losses requires a much finer level of resolution, namely that at the component level. If the network one level below the microgrid, i.e. the nanogrid, operated completely on DC power, these losses could be significantly reduced or nearly eliminated altogether. This network can be composed of appliances and equipment within a single building, coupled with an energy storage device and localized DERs to produce power when feasible. In addition, a grid-tie to the outside AC network can be utilized when necessary to power devices, or satisfy storage needs. </p> <p> </p> <p>This research demonstrates the novel implementation of a DC nanogrid within a residential setting known as <i>The DC Nanogrid House</i>, encompassing a complete household conversion from AC to DC power. The DC House functions as a veritable living laboratory, housing three graduate students living and working normally in the home. Within the house, a nanogrid design is developed in partnership with renewable energy generation, and controlled through an Energy Management System (EMS). The EMS developed in this project manages energy distribution throughout the house and the bi-directional inverter tied to the outside power grid. Alongside the nanogrid, household appliances possessing a significant yearly energy consumption are retrofitted to accept DC inputs. These modified appliances are tested in a laboratory setting under baseline conditions, and compared against AC equivalent original equipment manufacturer (OEM) models for power and performance analysis. Finally, the retrofitted devices are then installed in the DC Nanogrid House and operated under normal living conditions for continued evaluation.</p> <p> </p> <p>To complement the DC nanogrid, a comprehensive sensing network of IoT devices are deployed to provide room-by-room fidelity of building metrics, including proximity, air quality, temperature and humidity, illuminance, and many others. The IoT system employs Power over Ethernet (PoE) technology operating directly on DC voltages, enabling simultaneous communication and energy supply within the nanogrid. Using the aggregation of data collected from this network, machine learning models are constructed to identify additional energy saving opportunities, enhance overall building comfort, and support the safety of all occupants.</p><br><p></p>

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Electric transportation has been a well-studied research topic with electric ships gaining momentum. Ships can have a wide range in size from small cargo ships to military vessels. The benefits of electrification include meeting environmental sustainability goals and operational benefits in terms of flexibility and renewed operation. The power systems onboard a ship can be considered a microgrid, which is called a shipboard microgrid. This system poses unique challenges compared to land-based microgrids due to the resiliency requirements of being at sea. A control system for a hybrid- electric ship is proposed with both an energy storage system (ESS) and traditional diesel generators and gas turbines. This system balances economics with resilient control by calculating a baseline load distribution using the cost of operating each unit for the expected load profile. Additionally, the control system ensures that the generation capacity is available if the load does not follow the expected profile. To maintain flexibility, the system will redispatch the units as needed based on the actual load applied, while reducing the control efforts and maintaining the generation contingency. Therefore, the proposed shipboard microgrid control offers a control method that considers the cost of operation while maintaining the required standards of shipboard microgrid control. / Electrical and Computer Engineering

This dissertation studies two important models in the field of the distributed generation technologies to provide resiliency to the electric power distribution system. In the first part of the dissertation, we study the impact of assessing a Combined Cooling Heating Power system (CCHP) on the optimization and management of an on-site energy system under stochastic settings. These mathematical models propose a scalable stochastic decision model for large-scale microgrid operation formulated as a two-stage stochastic linear programming model. The model is solved enhanced algorithm strategies for Benders decomposition are introduced to find an optimal solution for larger instances efficiently. Some observations are made with different capacities of the power grid, dynamic pricing mechanisms with various levels of uncertainty, and sizes of power generation units. In the second part of the dissertation, we study a mathematical model that designs a Microgrid (MG) that integrates conventional fuel based generating (FBG) units, renewable sources of energy, distributed energy storage (DES) units, and electricity demand response. Curtailment of renewable resources generation during the MG operation affects the long-term revenues expected and increases the greenhouses emission. Considering the variability of renewable resources, researchers should pay more attention to scalable stochastic models for MG for multiple nodes. This study bridges the research gap by developing a scalable chance-constrained two-stage stochastic program to ensure that a significant portion of the renewable resource power output at each operating hour will be utilized. Finally, some managerial insights are drawn into the operation performance of the Combined Cooling Heating Power and a Microgrid.

Shipboard Power System (SPS) is known as an independent controlled small electric network powered by the distributed onboard generation system. Since many electric components are tightly coupled in a small space and the system is not supported with a relatively stronger grid, SPS is more susceptible to unexpected disturbances and physical damages compared to conventional terrestrial power systems. Among different distribution configurations, power-electronic based DC distribution is considered the trending technology for the next-generation U.S. Navy fleet design to replace the conventional AC-based distribution. This research presents appropriate control management frameworks to improve the Medium-Voltage DC (MVDC) shipboard power system performance. Model Predictive Control (MPC) is an advanced model-based approach which uses the system model to predict the future output states and generates an optimal control sequence over the prediction horizon. In this research, at first, a centralized MPC is developed for a nonlinear MVDC SPS when a high-power pulsed load exists in the system. The closed-loop stability analysis is considered in the MPC optimization problem. A comparison is presented for different cases of load prediction for MPC, namely, no prediction, perfect prediction, and Autoregressive Integrated Moving Average (ARIMA) prediction. Another centralized MPC controller is also designed to address the reconfiguration problem of the MVDC system in abnormal conditions. The reconfiguration goal is to maximize the power delivered to the loads with respect to power balance, generation limits and load priorities. Moreover, a distributed control structure is proposed for a nonlinear MVDC SPS to develop a scalable power management architecture. In this framework, each subsystem is controlled by a local MPC using its state variables, parameters and interaction variables from other subsystems communicated through a coordinator. The Goal Coordination principle is used to manage interactions between subsystems. The developed distributed control structure brings out several significant advantages including less computational overhead, higher flexibility and a good error tolerance behavior as well as a good overall system performance. To demonstrate the efficiency of the proposed approach, a performance analysis is accomplished by comparing centralized and distributed control of global and partitioned MVDC models for two cases of continuous and discretized control inputs.