SOLV lab

3. Forecast and Map Power Restoration Time Following Extreme Events (02/2021-12/2022)

Sponsor: Department of Energy, Office of Cybersecurity, Energy Security and Emergency Response

Finished tasks: Multi-agent based simulation for power restoration time estimation post extreme event

The multi-agent based simulation was developed to estimate a range of restoration times based on simulation, which could be used for decision-making and resource planning. Although, several tools and frameworks available for agent-based modeling, such as NetLogo, Repast, and MASON, these tools have proprietary coding language and environment. For our model, we used the open-source Mesa simulation package with Python that allows users to create ABMs using built-in core components (e.g., agent schedulers and spatial grids) or customized implementations; visualize them using a browser-based interface; analyze their results using Python’s data analysis tools; and incorporate spatial representation of the environment for agent interaction

Figure 1: substation spatial network

Figure 2: simulation steps range plot with/without road damage (crew team = 35)

Figure 3: Restoration time range map at county level

2. Ground level Integrated Diverse Energy Storage (GLIDES) (04/2018-11/2022)

Sponsor: Department of Energy, Water Power Technology Office

Finished tasks: 1) To estimate the market revenue potential of GLIDES, 2) design and optimize GLIDES integrated operation at building level, community level and utility level

Li-ion and Zn-air batteries are amongst the most promising technologies for buildings due to exceptionally high energy density. On the other hand, pumped-hydro energy storage (PHS) has the longest lifetimes, but they are dependent on suitable topographical conditions and have relatively lower overall efficiency. To overcome the disadvantage of PHS and address the need for dispatchable high-roundtrip efficiency energy storage, the concept of modular pumped hydro storage is recently introduced and invented as GLIDES which has been prototyped in Oak Ridge National Lab.

Figure 4: Basic layout and components of GLIDES system

A typical GLIDES system consists of a liquid storage reservoir, pre-pressurized pressure vessel(s), a pump/motor, and a hydraulic turbine/generator, see Figure 1. In the charging process of the GLIDES system, the electricity is stored by using pump/motor to pump liquid (e.g. water, oil, etc.) into the pressure vessel with pre-pressurized gas (e.g. air, carbon dioxide, etc.). To recover the stored energy in the discharging process, the now high-head water will be pushed by high pressure gas through the hydraulic turbine to4drive the electricity generator.

Figure 5: Prototypes of first and second generation GLIDES, a) pressure vessels, b) pelton turbine, c) IR image in charging, d) charging pump/motor, e) electric generator

The first-generation GLIDES prototype (see Figure 5) has been designed to operate between 70-130 bar pressure range with four 500 L gas tanks. In accounting for pump/motor, turbine and electric generator losses, the 91% indicated efficiency reduces to 66% round-trip efficiency for the first-generation GLIDES.

For second generation GLIDES, 84% round-trip efficiency could be achieved. Please note that the round-trip efficiency greatly depends on different configuration and turbomachinery efficiencies

several advantages of GLIDES: 1) it can be installed at ground level or below, basically anywhere that can structurally support of pressure vessels; 2) it has the ability to integrate a diverse range of low grade heat sources and use the waste heat to boost efficiency in discharging process; 3) it’s scalable which makes it possible to be allocated and utilized at grid-scale or equipped for smart buildings for behind-meter applications.

1. Hierarchical MFC Transactive Control of Building Demand Response (11/2018-11/2021)

Sponsor: Department of Energy, Building Technology Office

Finished tasks: proposes an efficient Demand Response management strategy incorporating emerging Distribution Locational Marginal Price (DLMP) based on a hierarchical transactive control approach

With grid modernization efforts, future distribution networks, which consist of various distributed generators and flexible loads, will be more flexible and active. All new network components of distributed energy resources (DERs) drive and enable the transition towards a market-based distribution network that seeks the optimal allocation of all DERs. To address challenges associated with DERs, one promising solution is to utilize demand-side flexibility of building loads facilitated by demand response (DR) programs and provide ancillary grid services through distribution-level markets.

Figure 6: Overiew of hierarchical transactional control approach

Figure 7: Flowchart of the proposed two-layer framework.

As shown in Figure 6, the proposed approach utilizes hierarchical transactional control architecture that includes three parties: the Distribution System Operator (DSO), Load Aggregators (LAs), and end users

In the proposed two-layer model flowchart Figure 7, at the upper layer, the proposed Optimal Demand Response (ODR) strategy is formulated as a bilevel model, and it incorporates the coupling between the DSO and LAs through the Demand Response program and DLMP within the limits of the aggregate flexibility offered by the participating flexible loads. The lower layer conducts the Model Free Control strategy with an aggregated ODR profile determined in the upper layer.

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