The Evolving Role of Distributed Energy Resources (DERs) in Power Systems
Distributed energy resources (DERs), including solar photovoltaics (PVs), wind turbines, fuel cells, energy storage systems (ESSs), and electric vehicles (EVs), are gaining prominence in power systems due to their advantages in improving energy efficiency, reducing carbon emissions, and enhancing grid resilience. Despite the increasing deployment, the potential of DERs has yet to be fully explored and exploited. A fundamental challenge restrains the management of numerous DERs in large-scale power systems: “How should DER data be securely processed, and DER operations be efficiently optimized?”
To address this critical question, this article considers two key issues: privacy for processing DER data and scalability in optimizing DER operations. It surveys existing and emerging solutions from a multi-agent framework perspective, highlighting the importance of achieving both scalability and privacy protection when deploying advanced DER control strategies.
Multi-Agent Frameworks for DER Control
The management of DERs in power systems can be viewed as the control of agents within a networked multi-agent system. A multi-agent system can be described by an Optimization model that specifies the problem objectives and constraints, and an Information exchange model that details the agents’ information exchange structure.
The Optimization model includes cooperative (for the system) and/or competitive objectives (between agents), subject to networked constraints (related to a set of agents) and/or local constraints (related to individual agents). The Information exchange model defines the computing and communication structure for solving the multi-agent problem, which can be classified into centralized, distributed, and decentralized structures.
Centralized approaches are easy to implement but suffer from computing and communication overloads, compromised data privacy, and vulnerability during contingencies. In contrast, distributed and decentralized multi-agent frameworks offer scalability, resilience, and enhanced privacy and cybersecurity, making them well-suited for large-scale DER control problems.
However, the frequent exchange of private information in these multi-agent frameworks renders the system and agents vulnerable to privacy breaches. To protect the privacy of stakeholders, it is essential to integrate privacy preservation techniques into the design of scalable multi-agent frameworks.
Formulating the DER Control Problem as a Multi-Agent Optimization
The DER control problem can be described as a multi-agent optimization model that defines the objectives, constraints, and decision variables to optimize power grid operations. Broadly, the problem can be formulated as:
Problem (P1):
minimize ∑i∈ℐ fi(xi) + g(x)
subject to xi ∈ Xi, ∀i∈ℐ
g(x) ≤ 0
where ℐ denotes the set of agents, xi is the decision variable of the ith agent, fi(·) is the individual objective function, g(·) is the coupled objective function, and Xi is the feasible set of xi.
The cooperative grid-level objectives support system-wide goals, such as load shifting, voltage regulation, and emission reduction. The competitive DER-level objectives aim to maximize the benefits of individual DERs, such as energy cost savings and battery degradation minimization.
Scalable Multi-Agent Frameworks for DER Control
This article reviews state-of-the-art and fundamental scalable algorithms within multi-agent frameworks, highlighting their applications in DER control problems. The key approaches include:
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Average Consensus (AvgC): AvgC-based algorithms enable agents to collaboratively compute the average of their local values in a distributed manner, facilitating cooperative decision-making in DER control problems.
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Alternating Direction Method of Multipliers (ADMM): ADMM decomposes the original large-scale problem into smaller subproblems, allowing parallel computing and communications among agents, making it well-suited for large-scale DER control.
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Projected Gradient Descent (PGD): PGD-based methods are effective in solving constrained optimization problems, particularly for large-scale DER control tasks with numerous local constraints and continuously differentiable objective functions.
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Multi-Agent Reinforcement Learning (MARL): MARL approaches enable data-driven decision-making for power systems with proliferating DERs, where various grid components can act as independent agents and operate within the grid environment.
These scalable multi-agent frameworks demonstrate outstanding performance in managing DER-populated power grids, addressing challenges such as high uncertainty, non-convexity, and large-scale optimization.
Privacy Preservation in Scalable Multi-Agent Frameworks
The acquisition, processing, and transmission of private customer data (e.g., energy consumption patterns, demographic data, locations) are typically necessary for delivering grid services and enhancing customer satisfaction. However, unauthorized usage of private data can lead to privacy leakages and malicious manipulation of the system, introducing vulnerabilities that hinder the deployment of advanced DER control approaches.
This article identifies three typical adversaries in multi-agent-based DER control problems:
- External eavesdroppers who wiretap and intercept the communication channels of the power systems.
- Honest-but-curious agents who follow the problem-solving procedures but are curious and try to infer the privacy of other participants.
- System operators (SOs) and/or coordinators/aggregators who have access to critical system information and may attempt to learn the DERs’ decision variables.
To prevent privacy leakage, this article explores mainstream and emerging privacy preservation techniques, including:
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Differential Privacy (DP): DP-based methods add well-calibrated noises to the computation process, obscuring the attributes of any single individual’s data without affecting grid-level and/or DER-level objectives.
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Encryption-Decryption (ED)-based Cryptosystems: ED-based methods utilize homomorphic cryptosystems that enable computations on encrypted data without decryption, preserving privacy during multi-agent communications.
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Secret Sharing (SS): SS-based protocols split a secret into multiple shares and distribute them among participants, ensuring that the secret can only be reconstructed by combining an adequate number of shares.
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Other Emerging Methods: Techniques such as state decomposition, noise injection, and garbled circuits also demonstrate the potential to achieve scalable and privacy-preserving multi-agent computing for DER control.
These privacy preservation techniques have been extensively studied and integrated within the design of scalable multi-agent frameworks, showcasing their effectiveness in protecting sensitive information while enabling efficient DER management.
Future Directions for Scalable, Privacy-Aware, and Cybersecure Multi-Agent Frameworks
As the electric power sector continues to evolve with the increasing penetration of DERs, several research directions present promising opportunities to unlock the full potential of DERs:
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Enhancing Accuracy, Privacy, Security, and Efficiency: Developing scalable and privacy-preserving algorithms with comprehensively enhanced performance, balancing or eliminating the trade-offs between accuracy, privacy, security, computing, and communication efficiency.
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Establishing Trustworthiness Across Fields: Consolidating knowledge from various fields, such as environmental science, human factors, behavioral science, and artificial intelligence, to strengthen the trustworthiness of multi-agent frameworks for DER control problems.
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Developing Zero-Trust Standards: Leveraging high-standard privacy and security concepts, such as zero-trust architectures, to create holistic privacy-aware and cybersecure frameworks that can handle both passive and active adversaries, ensuring multi-layer internal, external, and hierarchical protection for DER-populated power systems.
By exploring these research directions, the power and energy community can develop scalable, privacy-preserving, and cybersecure multi-agent frameworks that unlock the full potential of DERs, contributing to greater grid sustainability, security, and resilience.
Conclusion
The management of DERs in power systems can be viewed as the control of agents within a networked multi-agent system. Achieving both scalability and privacy protection is crucial when deploying advanced DER control strategies. This article has provided a comprehensive review of scalable and privacy-preserving multi-agent frameworks and their applications in DER control problems.
The article has explored various scalable multi-agent approaches, including average consensus, ADMM, PGD, and MARL, highlighting their effectiveness in managing DER-populated power grids. Furthermore, it has identified typical adversaries and examined mainstream privacy preservation techniques, such as differential privacy, encryption-decryption-based cryptosystems, and secret sharing, as well as other emerging methods.
Looking ahead, the power and energy community faces exciting opportunities to unlock the full potential of DERs by developing scalable, privacy-aware, and cybersecure multi-agent frameworks. Key research directions include enhancing performance, establishing trustworthiness across fields, and designing zero-trust standards. By addressing these challenges, the industry can enable greater grid sustainability, security, and resilience, ultimately benefiting both utility providers and end-users.
