Energy Optimization and Coordination Frameworks for Smart Homes Considering Incentives From Discomfort and Market Analysis

The electricity demand is increasing with the growing use of electricity-based appliances in today’s world. The residential sector’s electricity consumption share is also increasing. Demand response (DR) is a typical way to schedule consumers’ energy consumption and help utility to reduce the peak load demand. Residential demand management can contribute to reduce peak electric demand, decrease electricity costs, and maintain grid reliability. Though the demand management has benefits to the utility and the consumers, controlling the consumers electricity consumption provides inconvenience to the consumers. The challenge here is to properly address the customers’ inconvenience to encourage them to participate and meanwhile satisfy the required demand reduction efficiently. In this work, new incentive-based demand management schemes for residential houses are designed and implemented. This work investigates two separate DR frameworks designed with different demand reduction coordination strategies. The first framework design constitutes a utility, several aggregators, and residential houses participating in DR program. Demand response potential (DRP), an indicator of whether an appliance can contribute to the DR, guides the strategic allocation of the demand limit to the aggregators. Each aggregator aggregates the DRP of all the controllable appliances under it and sends to the utility. The utility allocates different demand limits to the aggregators based on their respective DRP ratios. Participating residential customers are benefited with financial compensation with consideration of their inconvenience. Two scenarios are discussed in this approach with DRP. One where the thermostatically controlled loads (TCLs) are controlled. The thermal comfort of residents and rewards are used to evaluate the demand response performance. The other scenario includes the time-shiftable appliances control with the same framework. The second framework is a three-level hierarchical control framework for large-scale residential DR with a novel bidding scheme and market-level analysis. It comprises of several residential communities, local controllers (LCs), a central controller (CC), and the electricity market. A demand reduction bidding strategy is introduced for the coordination among several LCs under a CC in this framework. Incentives are provided to the participating residential consumers, while considering their preferences, using a continuous reward structure. A simulation study on the 6-bus Roy Billinton Test System with 1;200 residential consumers demonstrates the financial benefits to both the electric utility and consumers

Coordinated Smart Home Thermal and Energy Management System Using a Co-simulation Framework

The increasing demand for electricity especially during the peak hours threaten the grid reliability. Demand response (DR), changing the load pattern of the consumer in response to system conditions, can decrease energy consumption during periods of high wholesale market price and also maintain system reliability. Residential homes consume 38% of the total electric energy in the U.S., making them promising for DR participation. Consumers can be motivated to participate in DR programs by providing incentives (incentive-based DR), or by introducing a time-varying tariff for electricity consumption (price-based DR). A home energy management system (HEMS), an automated system which can alter the residential consumer’s energy consumption pattern based on the price of electricity or financial incentives, enables the consumers to participate in such DR programs. HEMS also should consider consumer comfort during the scheduling of the heating, ventilation, and air conditioning (HVAC) and other appliances. As internal heat gain of appliances and people have a significant effect in the HVAC energy consumption, an integrated HVAC and appliance scheduling are necessary to properly evaluate potential benefits of HEMS. This work presents the formulation of HEMS considering combined scheduling of HVAC and appliances in time-varying tariff. The HEMS also considers the consumer comfort for the HVAC and appliances while minimizing the total electricity cost. Similarly, the HEMS also considers the detailed building model in EnergyPlus, a building energy analysis tool, to evaluate the effectiveness of the HEMS. HEMS+, a communication interface to EnergyPlus, is designed to couple HEMS and EnergyPlus in this work. Furthermore, a co-simulation framework coupling EnergyPlus and GridLAB-D, a distribution system simulation tool, is developed. This framework enables incorporation of the controllers such as HEMS and aggregator, allowing controllers to be tested in detail in both building and power system domains. Lack of coordination among a large number of HEMS responding to same price signal results in peak more severe than the normal operating condition. This work presents an incentive-based hierarchical control framework for coordinating and controlling a large number of residential consumers’ thermostatically controlled loads (TCLs) such as HVAC and electric water heater (EWH). The potential market-level economic benefits of the residential demand reduction are also quantified

Data-Driven Test Cases for Sustainability Assessment of Smart Grid Initiatives in Organized Electricity Markets

The primary aim of this dissertation is to deliver a technique to augment power system test cases with realistic open-source data to represent a deregulated power system. These test cases are intended to be used by power system researchers who require a test case that is capable of performing economic and environmental analysis on a bulk-power level. These test cases are capable of estimating the cost of bulk-energy for economic analysis and harmful greenhouse gas (GHG) and air polluting (AP) emissions for environmental sustainability analysis. These cases are developed for simulations that are intended to be at the transmission level where the independent system operator (ISO) has control. In the second part of this dissertation, an aggregator based demand response (DR) model is studied as-a-service to the bulk-power market, and its economic benefit is estimated using the augmented test cases. The augmentation technique presented in this dissertation has three-layer data over the existing generator information in a test case. The first layer of augmented data replaces the cost functions of the test case generators with functions developed based on the generator offers from a real electricity market. An unsupervised learning technique had to be implemented to classify the market offer data because the identity of the generators is masked to honor a fair market policy. The offer data was converted to cost functions and is sampled statistically such that the test cases represent a similar generator supply curve as the real power system. In addition to the cost functions layer, the test case generator data has an augmented generator fuel-turbine data. This data in a test case will represent the energy sources and generator technology of the system that the test case is intended to emulate. The hourly energy mix of the electricity market is utilized to augment the generator fuel-turbine type of test case generators. Because the number and capacities of test case generators may not represent the real system, assigning one fuel-turbine type to one test case generator will not result in a right energy mix. The augmentation technique creates an additional layer of information for each test case generator which can represent multiple fuel-types. The third layer of augmented data on test cases contains the heat curve and emission information. With all these layers of data, the test case is capable of representing the dynamic cost nature of a deregulated power system and is able to dispatch generators similar to the real power system. PJM interconnection data was chosen to implement the proposed augmentation technique. The marginal cost result from optimal power flow (OPF) is compared with the marginal cost of energy of the PJM interconnection along with the GHG and AP emissions. Smart-grids have opened opportunities for end customers to participate in the power system operation. DR is one of the activities that the end customers can perform to participate in the electricity market. Revenue earned from energy markets has been relatively low compared to DR used for capacity markets and ancillary services. An aggregated DR model participating in the bulk-power market as a service through a pool-based entity called demand response exchange (DRX) is proposed to improve the benefits of DR to the market. The economic benefits to the market entities have been studied using the proposed augmented test cases. The key contributions of this dissertation are: power systems test case generator data for researchers who do not have access to the real power system data, a technique that utilizes only open-source data to develop augmented data for any test case to represent the dispatch of a real power system in terms of cost, and emissions, a DR model capable of improving the revenue for DR participants in the bulk-energy market