Enabling Micro-level Demand-Side Grid Flexiblity in Resource Constrained Environments

The increased penetration of uncertain and variable renewable energy presents various resource and operational electric grid challenges. Micro-level (household and small commercial) demand-side grid flexibility could be a cost-effective strategy to integrate high penetrations of wind and solar energy, but literature and field deployments exploring the necessary information and communication technologies (ICTs) are scant. This paper presents an exploratory framework for enabling information driven grid flexibility through the Internet of Things (IoT), and a proof-of-concept wireless sensor gateway (FlexBox) to collect the necessary parameters for adequately monitoring and actuating the micro-level demand-side. In the summer of 2015, thirty sensor gateways were deployed in the city of Managua (Nicaragua) to develop a baseline for a near future small-scale demand response pilot implementation. FlexBox field data has begun shedding light on relationships between ambient temperature and load energy consumption, load and building envelope energy efficiency challenges, latency communication network challenges, and opportunities to engage existing demand-side user behavioral patterns. Information driven grid flexibility strategies present great opportunity to develop new technologies, system architectures, and implementation approaches that can easily scale across regions, incomes, and levels of development

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

Fast and Reliable Primary Frequency Reserves From Refrigerators with Decentralized Stochastic Control

Due to increasing shares of renewable energy sources, more frequency reserves are required to maintain power system stability. In this paper, we present a decentralized control scheme that allows a large aggregation of refrigerators to provide Primary Frequency Control (PFC) reserves to the grid based on local frequency measurements and without communication. The control is based on stochastic switching of refrigerators depending on the frequency deviation. We develop methods to account for typical lockout constraints of compressors and increased power consumption during the startup phase. In addition, we propose a procedure to dynamically reset the thermostat temperature limits in order to provide reliable PFC reserves, as well as a corrective temperature feedback loop to build robustness to biased frequency deviations. Furthermore, we introduce an additional randomization layer in the controller to account for thermostat resolution limitations, and finally, we modify the control design to account for refrigerator door openings. Extensive simulations with actual frequency signal data and with different aggregation sizes, load characteristics, and control parameters, demonstrate that the proposed controller outperforms a relevant state-of-the-art controller.Comment: 44 pages, 17 figures, 9 Tables, submitted to IEEE Transactions on Power System

Centralized and Decentralized Optimal Control of Variable Speed Heat Pumps

Utility service providers are often challenged with the synchronization of thermostatically controlled loads. Load synchronization, as a result of naturally occurring and demand-response events, has the potential to damage power distribution equipment. Because thermostatically controlled loads constitute most of the power consumed by the grid at any given time, the proper control of such devices can lead to significant energy savings and improved grid stability. The contribution of this paper is the development of an optimal control algorithm for commonly used variable speed heat pumps. By means of selective peer-to-peer communication, our control architecture allows for the regulation of home temperatures while simultaneously minimizing aggregate power consumption, and aggregate load volatility. An optimal centralized controller is also explored and compared against its decentralized counterpart

Demand response from thermostatically controlled loads: modelling, control and system-level value

The research area of this thesis concerns the efficient and secure operation of the future low-carbon power system, where alternative sources of control and flexibility will progressively replace the traditional providers of ancillary services i.e. conventional generators. Various options are engaged in this challenge and suit the innovative concept of Smart Grid. Specifically, this thesis investigates the potential of demand side response support by means of thermostatically controlled loads (TCLs). This thesis aims to quantify the impact that a population of thermostatically controlled loads has on the commitment and dispatch of a future power system characterized by a large penetration of renewable energy sources (e.g. wind) that are variable and intermittent. Thanks to their relative insensitivity to temperature fluctuations, thermostatic loads would be able to provide frequency response services and other forms of system services, such as energy arbitrage and congestion relief. These actions in turn enhance the power system operation and support the strict compliance with system security standards. However, the achievement of this transition requires addressing two challenges. The first deals with the design of accurate device models. Significant differences affect the devices’ design included in the same class, leading to different system-level performances. In addition, the flexibility associated to TCLs would be handled more easily by means of models that describes the TCLs dynamics directly as a cluster rather than considering the appliances individually. Second, it is not straightforward achieving satisfactory controllability of a cluster of TCLs for the considered applications. The complexity lies in the typical operation of these devices that has only two power states (on and off) whereas the desired response is continuous. Moreover the control strategy has always to comply with strict device-level temperature constraints as the provision of ancillary services cannot affect the quality of the service of the primary function of TCLs. This thesis addresses the challenges exhibited. Detailed thermal dynamic models are derived for eight classes of domestic and commercial refrigeration units. In addition, a heterogeneous population of TCLs is modelled as a leaky storage unit; this unit describes the aggregate flexibility of a large population of TCLs as a single storage unit incorporating the devices’ physical thermal models and their operational temperature limits. The control problem is solved by means of an initial hybrid controller for frequency response purposes that is afterwards replaced by an advanced controller for various applications. Provided these two elements, a novel demand side response model is designed considering the simultaneous provision of a number of system services and taking into account the effect of the load energy recovery. The model, included in a stochastic scheduling routine, quantifies the system-level operational cost and wind curtailment savings enabled by the TCLs support.Open Acces