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

Optimal provision of distributed reserves under dynamic energy service preferences

We propose and solve a stochastic dynamic programming (DP) problem addressing the optimal provision of regulation service reserves (RSR) by controlling dynamic demand preferences in smart buildings. A major contribution over past dynamic pricing work is that we pioneer the relaxation of static, uniformly distributed utility of demand. In this paper we model explicitly the dynamics of energy service preferences leading to a non-uniform and time varying probability distribution of demand utility. More explicitly, we model active and idle duty cycle appliances in a smart building as a closed queuing system with price-controlled arrival rates into the active appliance queue. Focusing on cooling appliances, we model the utility associated with the transition from idle to active as a non-uniform time varying function. We (i) derive an analytic characterization of the optimal policy and the differential cost function, and (ii) prove optimal policy monotonicity and value function convexity. These properties enable us to propose and implement a smart assisted value iteration (AVI) algorithm and an approximate DP (ADP) that exploits related functional approximations. Numerical results demonstrate the validity of the solution techniques and the computational advantage of the proposed ADP on realistic, large-state-space problems