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Distributed energy resources (DERs) are gaining prominence 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 question 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 question, this paper considers two critical issues, namely privacy for processing DER data and scalability in optimizing DER operations, then surveys existing and emerging solutions from a multi-agent framework perspective. In the context of scalability, this paper reviews state-of-the-art research that relies on parallel control, optimization, and learning within distributed and/or decentralized information exchange structures, while in the context of privacy, it identifies privacy preservation measures that can be synthesized into the aforementioned scalable structures. Despite research advances in these areas, challenges remain because these highly interdisciplinary studies blend a wide variety of scalable computing architectures and privacy preservation techniques from different fields, making them difficult to adapt in practice. To mitigate this issue, this paper provides a holistic review of trending strategies that orchestrate privacy and scalability for large-scale power system operations from a multi-agent perspective, particularly for DER control problems. Furthermore, this review extrapolates new approaches for future scalable, privacy-aware, and cybersecure pathways to unlock the full potential of DERs through controlling, optimizing, and learning generic multi-agent-based cyber-physical systems.

Reinforcement learning (RL)-based methods have achieved significant success in managing grid-interactive efficient buildings (GEBs). However, RL does not carry intrinsic guarantees of constraint satisfaction, which may lead to severe safety consequences. Besides, in GEB control applications, most existing safe RL approaches rely only on the regularisation parameters in neural networks or penalty of rewards, which often encounter challenges with parameter tuning and lead to catastrophic constraint violations. To provide enforced safety guarantees in controlling GEBs, this paper designs a physics-inspired safe RL method whose decision-making is enhanced through safe interaction with the environment. Different energy resources in GEBs are optimally managed to minimize energy costs and maximize customer comfort. The proposed approach can achieve strict constraint guarantees based on prior knowledge of a set of developed hard steady-state rules. Simulations on the optimal management of GEBs, including heating, ventilation, and air conditioning (HVAC), solar photovoltaics, and energy storage systems, demonstrate the effectiveness of the proposed approach.

The electric vehicle (EV) industry is rapidly evolving owing to advancements in smart grid technologies and charging control strategies. While EVs are promising in decarbonizing the transportation system and providing grid services, their widespread adoption has led to notable and erratic load injections that can disrupt the normal operation of power grid. Additionally, the unprotected collection and utilization of personal information during the EV charging process cause prevalent privacy issues. To address the scalability and data confidentiality in large-scale EV charging control, we propose a novel decentralized privacy-preserving EV charging control algorithm via state obfuscation that 1) is scalable w.r.t. the number of EVs and ensures optimal EV charging solutions; 2) achieves privacy preservation in the presence of honest-but-curious adversaries and eavesdroppers; and 3) is applicable to eliminate privacy concerns for general multi-agent optimization problems in large-scale cyber-physical systems. The EV charging control is structured as a constrained optimization problem with coupled objectives and constraints, then solved in a decentralized fashion. Privacy analyses and simulations demonstrate the efficiency and efficacy of the proposed approach.

Distributed and decentralized multi-agent optimization (DMAO) algorithms enable the control of large-scale grid-edge resources, such as electric vehicles (EV), to provide power grid services. Despite its great scalability, DMAO is fundamentally prone to cyber attacks as it is highly dependent on frequent peer-to-peer communications. Existing cyber-security research in this regard mainly focuses on \emph{broad-spectrum} attacks aiming at jeopardizing the entire control system while losing the possibility of achieving specific attacking purposes. This paper, for the first time, explores novel \emph{for-purpose} algorithmic attacks that are launched by participating agents and interface with DMAO to achieve self-interest attack purposes. A decentralized EV charging control problem is formulated as an illustrative use case. Theoretical \emph{for-purpose} attack vectors with and without the stealthy feature are devised. Simulations on EV charging control show the practicability of the proposed algorithmic \emph{for-purpose} attacks and the impacts of such attacks on distribution networks., Comment: 6 pages, 6 Figures

The rapidly growing penetration of renewable energy resources brings unprecedented challenges to power distribution networks - management of a large population of grid-tied controllable devices encounters control scalability crises and potential end-user privacy breaches. Despite the importance, research on privacy preservation of distributed energy resource (DER) control in a fully scalable manner is lacked. To fill the gap, this paper designs a novel decentralized privacy-preserving DER control framework that 1) achieves control scalability over DER population and heterogeneity; 2) eliminates peer-to-peer communications and secures the privacy of all participating DERs against various types of adversaries; and 3) enjoys higher computation efficiency and accuracy compared to state-of-the-art privacy-preserving methods. A strongly coupled optimization problem is formulated to control the power consumption and output of DERs, including solar photovoltaics and energy storage systems, then solved using the projected gradient method. Cloud computing and secret sharing are seamlessly integrated into the proposed decentralized computing to achieve privacy preservation. Simulation results prove the capabilities of the proposed approach in DER control applications.

The flexible, efficient, and reliable operation of grid-interactive efficient buildings (GEBs) is increasingly impacted by the growing penetration of distributed energy resources (DERs). Besides, the optimization and control of DERs, buildings, and distribution networks are further complicated by their interconnections. In this paper, we exploit load-side flexibility and clean energy resources to develop a novel two-level hybrid decentralized-centralized (HDC) algorithm to control DER-connected GEBs. The proposed HDC 1) achieves scalability w.r.t. to a large number of grid-connected buildings and devices, 2) incorporates a two-level design where aggregators control buildings centrally and the system operator coordinates the distribution network in a decentralized fashion, and 3) improves the computing efficiency and enhances communicating compatibility with heterogeneous temporal scales. Simulations are conducted based on the prototype of a campus building at the Oak Ridge National Laboratory to show the efficiency and efficacy of the proposed approach., Comment: Submitted to L-CSS

Securing the power system from catastrophic natural disasters is a rising problem in power system operation and planning. This paper particularly considers earthquake and aims to evaluate and improve the resilience of power distribution networks by developing a novel hardware hardening framework. In the proposed framework, fragility curves of the network equipment are used to represent equipment failure probabilities when facing an earthquake, and failure scenarios of the distribution network are obtained via the Monte Carlo method. Based on the distribution network topology and the locations of essential loads, various hardware hardening strategies are determined within the proposed framework. Through a series of resilience and economic analyses, the optimal hardening strategy is determined to improve the resilience and supply the essential loads during and after the earthquake. The efficacy of the proposed approach is examined through simulations on an IEEE 33-bus test feeder., Comment: 6 pages, 4 figures

Cooperative electric vehicle (EV) charging control has emerged as a key component in grid-edge resource (GER) management. However, customers' privacy remains a major barrier to large-scale implementation of EV charging control. In this paper, we develop a distributed privacy-preserving EV charging control protocol based on secret sharing (SS) that 1) achieves scalability over EV population size; 2) enjoys higher computation efficiency compared to homomorphic encryption (HE) based methods; and 3) secures the privacy of the participating EVs against honest-but-curious adversaries and external eavesdroppers. The cooperative EV charging control is formulated to achieve overnight valley-filling and framed into the projected gradient algorithm (PGA) structure as a distributed optimization problem. SS is integrated into PGA to achieve secure updates of both primal and dual variables. Theoretical security analyses and simulations in a residential area are conducted to prove the privacy preservation guarantee as well as the efficacy and efficiency of the proposed privacy preservation method. Broadly, the proposed method can be readily extended to various GER control applications.

Large-scale multi-agent cooperative control problems have materially enjoyed the scalability, adaptivity, and flexibility of decentralized optimization. However, due to the mandatory iterative communications between the agents and the system operator, the decentralized architecture is vulnerable to malicious attacks and privacy breach. Current research on addressing privacy preservation of both agents and the system operator in cooperative decentralized optimization with strongly coupled objective functions and constraints is still primitive. To fill in the gaps, this paper proposes a novel privacy-preserving decentralized optimization paradigm based on Paillier cryptosystem. The proposed paradigm achieves ideal correctness and security, as well as resists attacks from a range of adversaries. The efficacy and efficiency of the proposed approach are verified via numerical simulations and a real-world physical platform.

Existing large-scale optimization schemes are challenged by both scalability and cyber-security. With the favorable scalability, adaptability, and flexibility, decentralized and distributed optimization paradigms are widely adopted in cyber-physical system applications. However, most existing approaches heavily rely on explicit information exchange between agents or between agents and the system operator, leading the entire framework prone to privacy risks. To tackle this issue, this paper synthesizes cryptography and decentralized optimization techniques to develop a novel privacy-preserving decentralized optimization paradigm. The proposed paradigm is generically applicable to strongly coupled convex optimization problems with nonseparable objective functions and linearly coupled constraints. The security and accuracy of the proposed paradigm are verified via numerical examples., Comment: Submitted to the 4th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS)

Cooperatively optimizing a vast number of agents that are connected over a large-scale network brings unprecedented scalability challenges. This paper revolves around problems optimizing coupled objective functions under coupled network-induced constraints and local constraints. The scalability of existing optimization paradigms is limited by either the agent population size or the network dimension. As a radical improvement, this paper for the first time constructs a two-facet scalable decentralized optimization framework. To this end, we first develop a systemic network dimension reduction technique to virtually cluster the agents and lower the dimension of network-induced constraints, then constitute a novel shrunken-primal-multi-dual subgradient (SPMDS) algorithm based on the reduced-dimension network. Rigorous optimality and convergence analyses of the proposed decentralized optimization framework are provided. The SPMDS-based optimization framework is free of agent-to-agent communication and no additional aggregators are required for agent clusters. The efficiency and efficacy of the proposed approaches are demonstrated, in comparison with benchmark methods, through simulations of electric vehicle charging control problems and traffic congestion control problems., Comment: arXiv admin note: substantial text overlap with arXiv:2003.14295

The charging processes of a large number of electric vehicles (EVs) require coordination and control for the alleviation of their impacts on the distribution network and for the provision of various grid services. However, the scalability of existing EV charging control paradigms are limited by either the number of EVs or the distribution network dimension, largely impairing EVs' aggregate service capability and applicability. To overcome the scalability barrier, this paper, motivated by the optimal scheduling problem for the valley-filling service, (1) proposes a novel dimension reduction methodology by grouping EVs (primal decision variables) and establishing voltage (global coupled constraints) updating subsets for each EV group in the distribution network and (2) develops a novel decentralized shrunken primal multi-dual subgradient (SPMDS) optimization algorithm to solve this reduced-dimension problem. The proposed SPMDS-based control framework requires no communication between EVs, reduces over 43% of the computational cost in the primal subgradient update, and reduces up to 68% of the computational cost in the dual subgradient update. The efficiency and efficacy of the proposed algorithm are demonstrated through simulations over a modified IEEE 13-bus test feeder and a modified IEEE 123-bus test feeder., Comment: 8 pages, 5 figures, 2 tables, submitted to 2020 IEEE CDC (submission 728)

In this paper, we develop a chance-constrained decentralized electric vehicle (EV) charging control framework to achieve "valley-filling" meanwhile meeting individual charging requirements and satisfying distribution network constraints. The control design is formulated as an optimization problem with a stochastic non-separable objective function and globally coupled chance constraints. We propose a novel chance-constrained shrunken-primal-dual subgradient (CC-SPDS) algorithm to support the chance-constrained decentralized control scheme and verify its efficacy and convergence with a representative distribution network model.

Scalable demand response of residential electric loads has been a timely research topic in recent years. The commercial coming of age or residential demand response requires a scalable control architecture that is both efficient and practical to use. This work presents such a strategy for domestic hot water heaters and present a commercial proof-of-concept deployment. The strategy combines state of the art in aggregate-and-dispatch with a novel dispatch strategy leveraging recent developments in reinforcement learning and is tested in a hardware-in-the-loop simulation environment. The results are promising and present how model-based and model-free control strategies can be merged to obtain a mature and commercially viable control strategy for residential demand response., Comment: 8 pages, 8 figures, submitted to IEEE Transactions on Smart Grid

Electric vehicle (EV) charging can negatively impact electric distribution networks by exceeding equipment thermal ratings and causing voltages to drop below standard ranges. In this paper, we develop a decentralized EV charging control scheme to achieve "valley-filling" (i.e., flattening demand profile during overnight charging), meanwhile meeting heterogeneous individual charging requirements and satisfying distribution network constraints. The formulated problem is an optimization problem with a non-separable objective function and strongly coupled inequality constraints. We propose a novel shrunken primal-dual subgradient (SPDS) algorithm to support the decentralized control scheme, derive conditions guaranteeing its convergence, and verify its efficacy and convergence with a representative distribution network model., Comment: 16 pages, 11 figures