A Composable Method for Real-Time Control of Active Distribution Networks with Explicit Power Setpoints

Abstract: The conventional approach for the control of distribution networks, in the presence of active generation and/or controllable loads and storage, involves a combination of both frequency and voltage regulation at different time scales. With the increased penetration of stochastic resources, distributed generation and demand response, this approach shows severe limitations in both the optimal and feasible operation of these networks, as well as in the aggregation of the network resources for upper-layer power systems. An alternative approach is to directly control the targeted grid by defining explicit and real-time setpoints for active/reactive power absorptions/injections defined by a solution of a specific optimization problem; but this quickly becomes intractable when systems get large or diverse. In this paper, we address this problem and propose a method for the explicit control of the grid status, based on a common abstract model characterized by the main property of being composable. That is to say, subsystems can be aggregated into virtual devices that hide their internal complexity. Thus the proposed method can easily cope with systems of any size or complexity. The framework is presented in this Part I, whilst in Part II we illustrate its application to a CIGR\'E low voltage benchmark microgrid. In particular, we provide implementation examples with respect to typical devices connected to distribution networks and evaluate of the performance and benefits of the proposed control framework.

• The paper introduces a composable method that uses optimized power setpoints via gradient descent to achieve real-time grid control.
• The paper leverages an abstract model to aggregate subsystems as virtual devices, enabling scalable and efficient management of complex networks.
• The paper demonstrates enhanced operational performance in low-voltage microgrids, supporting both islanded and grid-connected scenarios with stochastic resources.

A Composable Method for Real-Time Control of Active Distribution Networks with Explicit Power Setpoints

Introduction

This paper addresses the increasing complexities in managing active distribution networks as they accommodate a growing penetration of stochastic resources such as distributed generation and demand response. Traditional approaches using frequency and voltage regulation are becoming insufficient due to these networks' complexity and the need for real-time adaptability. Consequently, the paper proposes a novel method leveraging explicit real-time control through optimized power setpoints. This method allows subsystems to be treated as composable units, enabling efficient aggregation into virtual devices, thereby addressing scalability challenges.

Methodology

The approach is based on a common abstract model that facilitates the direct and explicit control of power injections and absorptions. This model includes several key features:

1. Abstract Framework: Subsidiary system capabilities and internal states are represented using a standardized abstraction. This representation includes $PQt$ profiles, virtual costs, and belief functions expressed in a common language that is device-independent.
2. Composition of Subsystems: Enables the aggregation of subsystems into virtual devices that simplify internal complexities, making the method scalable across systems of any size.
3. Separation of Concern: Different types of agents handle separate responsibilities—Grid Agents (GAs) manage grid behavior using abstract data from Resource Agents (RAs), which are responsible for specific resources.
4. Periodic Control Cycle: The system operates in predefined time cycles to ensure real-time adaptability to changes in grid conditions and generation/consumption.

Implementation

GAs use an optimization process to adjust setpoints at each cycle. The goal is to minimize virtual costs across the network, aligning with specified power flow targets and ensuring the grid operates in a feasible state. An essential component of the proposed methodology is the use of a gradient descent approximation that calculates new setpoints for control targets while respecting the constraints imposed by belief functions and allowable state parameters within the grid.

The central operations within the agent hierarchy include:

• Advertisement/Request Protocol: RAs and GAs utilize this protocol to communicate model states and setpoints. RAs advertise current capabilities and virtual costs, while GAs send optimized power injection targets.
• Agent Dynamics: The GAs' decision-making process involves projecting control targets into an admissible operating space that satisfies feasibility conditions, leveraging real-time state estimates and sensitivity coefficients.
• Optimization Challenges: The requirement to aggregate and simplify RAs into composite agents while maintaining efficiency in computation presents unique challenges. This includes approximating beliefs and ensuring that projections remain within feasible bounds.

Performance and Applications

Part II of the study illustrates this framework's application to a representative low-voltage microgrid, benchmarking its performance and operational benefits against current methodologies. This is accomplished by evaluating the framework's ability to manage diverse, fluctuating resources without extending the reliance on centralized, traditional power plants. Consequently, the method supports both islanded and grid-connected operations, paving the way for localized control efficiency.

Conclusion

The presented framework offers a robust solution for the real-time control of electrical networks, addressing both scalability and operational efficiency. Its composable methodology, abstract representation of grid components, and comprehensive optimization strategy form a scalable architecture capable of catering to modern power systems' evolving demands. Future developments could explore enhancing computational efficiency and integrating additional stability constraints to further refine real-time adaptability and system resilience.