Ancillary Service to the Grid Using Intelligent Deferrable Loads (1402.4600v1)

Abstract: Renewable energy sources such as wind and solar power have a high degree of unpredictability and time-variation, which makes balancing demand and supply challenging. One possible way to address this challenge is to harness the inherent flexibility in demand of many types of loads. Introduced in this paper is a technique for decentralized control for automated demand response that can be used by grid operators as ancillary service for maintaining demand-supply balance. A Markovian Decision Process (MDP) model is introduced for an individual load. A randomized control architecture is proposed, motivated by the need for decentralized decision making, and the need to avoid synchronization that can lead to large and detrimental spikes in demand. An aggregate model for a large number of loads is then developed by examining the mean field limit. A key innovation is an LTI-system approximation of the aggregate nonlinear model, with a scalar signal as the input and a measure of the aggregate demand as the output. This makes the approximation particularly convenient for control design at the grid level. The second half of the paper contains a detailed application of these results to a network of residential pools. Simulations are provided to illustrate the accuracy of the approximations and effectiveness of the proposed control approach.

• The paper introduces a Markovian Decision Process model enabling decentralized control of flexible loads for grid balancing.
• The paper employs a mean-field limit to approximate nonlinear aggregated load dynamics as an LTI system, simplifying control design.
• The paper validates its approach through residential pool simulations, illustrating effective ancillary service provision and grid reliability.

Intelligent Deferrable Loads as Ancillary Services to the Power Grid

The paper "Ancillary Service to the Grid Using Intelligent Deferrable Loads" addresses the integration of intelligent deferrable loads into power grids as a means of providing ancillary services, thereby enhancing grid stability, particularly amid rising penetration of renewable energy sources. This research presents a decentralized control strategy focused on automated demand response and posits how flexibility in energy consumption of various loads can be leveraged by grid operators to address fluctuations in supply-demand balance.

Demand Flexibility and Decentralized Control Approach

The authors introduce a Markovian Decision Process (MDP) model for individual loads, suggesting a randomized control architecture as a means to decentralize decision-making and prevent synchronization that could lead to demand spikes. This decentralized architecture allows each load to autonomously make decisions based on its state and a control signal from the balancing authority (BA), with the objective to seamlessly integrate demand-side resources into grid management without any disruption to consumer needs. The research postulates that many electric loads, such as those found in household appliances, exhibit inherent flexibility in their operational times, creating potential for these loads to provide valuable ancillary services without degrading the quality of the current service they offer.

Mean Field Limit and LTI System Approximation

One of the pivotal contributions of this paper lies in modeling the aggregate behavior of numerous loads using a mean-field approach. This enables an innovative Linear Time-Invariant (LTI) system approximation of the aggregate nonlinear model, wherein a scalar input signal manages the collective behavior of the loads towards achieving desired demand-supply equilibrium. By examining the mean-field limit, the authors derive an explicit LTI model that transforms the complex non-linear interactions of many loads into a more tractable form for control design. This approach significantly simplifies the control tasks for grid operators, enabling the use of well-established control techniques to regulate grid behavior.

Application to Residential Pools

The second segment of the paper applies these theoretical findings to the control of a network of residential pool pumps. Through simulations, the authors illustrate the accuracy of their model and the efficacy in controlling pool pumps to perform grid balancing functions. The simulations validate their approximations and the efficacy of decentralized control in real-world scenarios, demonstrating the potential of residential pools in contributing to medium-frequency grid regulation.

Implications and Future Directions

From a practical standpoint, this research points toward leveraging untapped resources within residential settings for grid support. The LTI-system approximation to model deferrable loads presents not only a theoretical advancement but also a promising pathway for utilities and grid operators to handle the intermittency associated with renewable energy sources. Future work can explore expanding this framework to a broader class of deferrable loads and examine the stakeholder collaboration necessary to implement these decentralized mechanisms effectively.

Conclusion

Overall, the paper underscores intelligent deferrable loads' capability in providing consistent, zero-energy ancillary services to power grids. The research bridges the gap between demand response programs and grid management, pushing forward theoretical insights into practical applications within the energy sector. The presented methodologies and findings offer intriguing avenues for advancing grid reliability and paving the way for wider adoption of renewable energy technologies.