The importance of flexible customer-side resources in transitioning to a clean energy future is becoming increasingly apparent. Flexible customer-side resources can resolve most issues associated with intelligent and low carbon power grids and, in the process, unlock new value streams for both resource owners and load-serving entities (LSEs) with access to those resources. However, most LSEs with access to numerous flexible customer-side resources often use them for single applications when these resources can provide multiple value streams simultaneously. This dissertation focuses on developing models and frameworks to help LSEs simultaneously capture multiple value streams from customer-side resources within their jurisdiction.
Firstly, a stochastic equivalent battery model (EBM) that provides a simple yet accurate representation of the overall power consumption flexibility associated with a commercial building is proposed. The proposed stochastic EBM combines model-based functional simulations and optimization techniques to quantify the overall flexibility of a commercial building with flexible resources such as heating, ventilation, and air-conditioning (HVAC), electric water heater (EWH), battery, and electric vehicle charging stations. Illustrative case studies showcasing how the proposed model fits into complex resource scheduling problems whose objectives either maximize or minimize some value reflecting the LSE’s intended outcomes are also considered.
Secondly, a stochastic optimization framework is proposed to help an LSE capture value streams involving bulk power system support services from its residential customer-side resources. The specific value streams of interest are energy arbitrage, peak shaving, and market-based frequency regulation, while the customer-side resources are residential HVACs, EWHs, and behind-the-meter (BTM) storage. A resource type-centric clustering method is employed. The proposed framework contains
two parts. The first part involves a day-ahead resource scheduling problem that captures uncertainties in energy prices, regulation prices, and frequency regulation signals. A voltage sensitivity matrix-based approach is proposed to capture the impacts of resource control actions on system voltages. The second part includes
two real-time resource dispatch algorithms capable of eliciting fast responses from the resources to frequency regulation signals from the market operator with minimal voltage violations. The scheduling model and dispatch algorithms are evaluated using a HELICS-based co-simulation platform and real-world market data from New York Independent System Operator (NYISO).
Thirdly, a stochastic optimization framework is proposed to help an LSE capture multiple value streams focused on distribution system operations from its residential customer-side resources. The value streams of interest are peak shaving, energy arbitrage, ramp rate reduction, loss reduction, and voltage management. The framework captures the impact of third-party aggregators on the LSE’s network and includes two dispatch algorithms - decision rule-based dispatch and optimal real-time dispatch.
Finally, a framework to help LSEs compensate owners of customer-side resources for multiple value streams is proposed. The compensation sharing approach classifies the LSE’s realized value into three categories - additive, super-additive and subadditive. The appropriate compensation-sharing mechanism is then defined for each value category. A special component of the compensation sharing mechanism that provides additional social benefits, specifically credit rating improvement, for low and medium-income flexible resource owners is also proposed.