Komeil Moghaddasi, Raja Jurdak, Sara Khalifa, Yuchen Zhang ¡ 7 authors
The rapid growth of distributed energy resources (DER) such as rooftop photovoltaics (PV), battery storage, electric vehicles (EV), and flexible loads, is shifting power system coordination from centralised control centres to millions of prosumers and local controllers at the distribution level. This transition has led to many new coordination approaches across control, market, and learning-based models, which are often described as decentralised. However, this term is applied inconsistently: it may refer to decomposed optimisation, edge computing, peer-to-peer (P2P) trading, or distributed ledger technology, obscuring what is actually being decentralised, authority, computation, information, or topology. Existing surveys typically address one such concept in isolation, for example, microgrid control structures, energy management system (EMS) topologies, or market designs, without providing a unified, multi-dimensional view across the full coordination landscape. In this survey, we propose a six-tier graduated decentralisation scale for distribution level coordination architectures, accompanied by a set of classification criteria that we apply to systematically map and compare recent architectures. We discuss how topology, decision-making, autonomy, intelligence, information flow, and coordination mechanisms evolve as architectures move from centralised to more decentralised operation. We further identify concrete research gaps, and outline future directions for deployment grade, multi-actor grid coordination.
Gary Hahn, Emilio C. Piesciorovsky, Raymond Borges Hink, Aaron Werth
Modern electrical grids face growing stability risks from customer-owned generators, especially at points of common couplings (PCCs). Disruptive behavior from power-electronic sources can cause protective relays to isolate problematic generators, making measurement integrity critical. This article presents a distributed ledger technology (DLT) approach that uses smart contracts to evaluate PCC voltage measurements and trigger backup breaker operations. The approach is framed as a verifiable, multi-organization attestation and audit layer, not as a real-time control security mechanism. In the proposed architecture, voltage measurements from a hardware protective relay are anchored on a DLT through the Cyber Grid Guard (CGG) system for attestation by both the grid utility and customer-owned generator. A Voltage Service Limits (VSLs) smart contract evaluates the on-chain measurements against allowable phase-voltage limits derived from the ANSI C84.1 standard. The framework is validated in a hardware-relay-in-the-loop test bed under sustained-undervoltage, sustained-overvoltage, and transient line-to-line fault scenarios. The results show that the VSL smart contract can process these measurements and issue backup breaker actions consistent with the defined service-limit criteria, demonstrating the DLT potential as a verifiable audit layer at the PCC that complements primary protection.
Inter-provincial electricity transactions within Chinaâs unified power market are complicated by spatial heterogeneity, asynchronous dispatch timelines, and strategic deviations in bilateral commitments. Existing mechanisms often struggle with ex-post contestability, temporal inconsistencies, and poor alignment between real-time system conditions and deviation pricing, undermining the marketâs fairness and reliability. To address these challenges, this paper proposes a novel Tri-Ledger Coordinated Settlement (TCS) framework with built-in temporal consistency. The tri-ledger design consists of (1) a Contract Ledger capturing day-ahead bilateral schedules, (2) a Dispatch Ledger reflecting system-level nodal redispatch outcomes, and (3) a Deviation Ledger reconciling discrepancies across provinces through an enforceable and tamper-resistant protocol. Central to this framework is a Distributionally Robust Deviation Pricing (DRDP) model, which penalizes deviation behaviors not based on deterministic thresholds but through ambiguity-aware dual pricing anchored in Wasserstein-ball uncertainty sets. This allows the pricing system to anticipate manipulative strategies while offering probabilistic fairness to genuine imbalances caused by renewables or congestion. Furthermore, a Non-Contestable Decoupled Execution Mechanism (NCDEM) is developed to isolate provincial profit zones during redispatch operations, ensuring that a province cannot benefit by manipulating its declared bilateral trades or influencing othersâ deviation compensations. The proposed approach guarantees strategy-proofness under minimal information assumptions and supports distributed execution by provincial grid companies without centralized re-optimization. The effectiveness of the framework is demonstrated on a stylized multi-province testbed derived from Chinaâs Eastern and Central grid clusters. Numerical experiments show that the DRDP-based settlement leads to over 18.4% improvement in fairness-adjusted social welfare and reduces strategic deviation incentives by up to 73% compared to deterministic baseline models. Sensitivity analyses validate robustness under multiple load and RES penetration scenarios. The proposed TCS framework offers policy-relevant insights for implementing transparent and resilient provincial electricity market settlements under Chinaâs âdual-trackâ trading architecture.
ABSTRACT This paper deals with the twoâplayer zeroâsum game problem, which is a bounded âgain robust control problem. Finding an analytical solution to the complex HamiltonâJacobiâIssacs (HJI) equation is a challenging task. Hence, a novel Qâlearning algorithm for unknown continuousâtime (CT) affineâinâinputs nonlinear systems is proposed for generating an approximate solution to the HJI equation, which is valid in a local domain due to the use of a local approximator, that is, a Neural Network (NN) structure. The approach is modelâfree and does not require the knowledge of system drift dynamics, and input and disturbance gains. The algorithm learns online from measurements of state variables in real time. To generate the local approximate solution of the HJI equation for the twoâplayer zeroâsum game problem for nonlinear systems, the proposed nonâiterative algorithm requires only a single critic NN instead of the commonly used triple NN approximator structure. A persistence of excitation condition is required to guarantee Uniformly Ultimately Boundedness (UUB) and convergence to the optimal solution. The effectiveness of the proposed Qâlearning approach for the twoâplayer zeroâsum game problem is demonstrated via simulations of a linear Fâ16 aircraft plant and a highly complex nonlinear system. Proof of closedâloop system stability is provided using Lyapunov Analysis, and convergence of the approximate solution to the true saddleâpoint solution is guaranteed in a UUBâsense.
The advancement and proliferation of digitalization and communication infrastructure have facilitated the rise of real-time bidding markets in smart grids. In these dynamic markets, energy distribution companies and power-generating companies interact to establish energy exchange contracts based on offered prices. However, the fluctuation in power flow resulting from contract changes within the real-time bidding market introduces a potential vulnerability that malicious attackers can exploit to launch successful stealthy attacks. To enhance the smart grid resiliency against cyber-attack in the power market bidding environment, a new barrier-function adaptive finite-time trajectory tracking control is proposed in this paper. The developed controller is utilized to actively counteract and mitigate potential cyber-attacks to ensure their rejection and prevention. The stability analysis convincingly demonstrates the rapid convergence of system states within a finite time frame, empowering the system to effectively reject cyber-attacks in real-time. Test results of an IEEE test systems, considering governor dead bound nonlinearity and communication time delay are presented and compared with those obtained from other methods to ensure and demonstrate the performance of proposed method. The Speedgoat real-time target machine, along with Simulink real-time, validates the effectiveness of the proposed method.
In this thesis, we categorize the challenges that Distribution System Operators (DSO) are facing into two separate sets of articles. After the introduction, the initial set of articles (chapters 2, 3, and 4) focuses on network operation, addressing challenges, and suggesting creative solutions to enhance the resilience and effectiveness of decentralized energy systems. The subsequent set of articles (chapters 5,6,7 and 8) redirects attention to the exploration of energy communities, unveiling the potential of localized, participatory energy ecosystems. Chapter 2 can be summarized as follows. In an electrical system where decentralized and embedded productions are becoming increasingly important, it is essential to ensure a good understanding of their behavior at their operating limits. One of the most important operating limits is when the system frequency approaches 50.2 Hz. At this frequency, following the old requirements, many existing European PV inverters have to be disconnected. In such situations, we demonstrate that the variance of the frequency measurement taken at every PV inverter plays a key role. It has been demonstrated that this variance is a good thing from the system's point of view as it allows for a gradual disconnection, leading to a controlled variation of the frequency. To address the challenges due to decentralized energy generation and emerging loads like electric vehicles, DSOs implement Active Network Management (ANM) as a short-term strategy to manage efficiently power injection and consumption, avoiding congestion without the need for heavy infrastructure investment. ANM requires knowledge of the system state, necessitating the placement of measurement devices throughout the network to ensure accurate estimates. In that context, chapter 3 introduces a new method for placing measurement devices in distribution networks. In contrast to the previous research works which rely on objectives for the placement such as state estimation accuracy, the proposed method incorporates ANM considerations in the process of determining the optimal locations, aiming to enhance ANM quality. Simulation results on a test distribution network demonstrate the superiority of this approach, leading to reduced curtailment of generators and improved overall performance. Grid monitoring strategies, like the one presented in Chapter 3 is the process of collecting data from sensors across a distribution grid and sending it to a central system (SCADA) to identify and diagnose problems, improve reliability, and save energy and money. The increasing complexity of power flows and the need to manage them using ANM strategies requires accurate data and strong defenses against cyber attacks. A proof-of-concept software called "MonitORES" was developed using Hyperledger Fabric to demonstrate how a distributed ledger technology (DLT) such as blockchain can be used to monitor and control generation units within ANM schemes, with improved resilience against cyberattacks. It is this work that is presented in the chapter 4. Chapter 5 opens the second set of articles aiming to explore renewable energy communities (REC). The main goal of the E-Cloud, one of the first projects of energy communities in Wallonia, as with every microgrid, is to maximize the consumption of energy produced locally. To reach this goal, based on consumption profiles of customers willing to participate in the E-cloud and given some local restrictions (e.g. wind turbines cannot be put everywhere), an optimal mix of green generation sources (in kW) and local storage (in kWh) needs to be computed. Then according to this computation, the required generating units and storage devices are installed. A repartition mechanism grants the customer a share of the generated electricity and storage capacity. These shares are either computed offline or dynamically adapted online. The project aimed to test two models: either the DSO or a producer owns and operates the storage device. Two information flows (real-time for the operation of the storage facility and ex-post for its settlement) are needed to ensure correct information exchange with the wholesale market. These information flows are completed thanks to a forecast that provides members of the E-Cloud the full capability to anticipate and obtain the maximum benefits of the local generation. The expected benefits for the customer are a reduction of their electricity bill by a minimum of 10\%. Societal benefits should also arise: 1) easing the technical integration of renewables generation embedded in the distribution network, and 2) avoiding extra investment in the DSO network. The next chapter proposes that the success of local REC, now foreseen by the European Union directives but also growing worldwide, will rely on the appetite of consumers and investors. This is not obvious when the target local area is a residential community where people have varying expectations. Based on Bayesian game theory (also called a game of incomplete information), the purpose of this paper is to define an approach for determining, from the point of view of the renewable energy investor, the level of production capacity and energy price that needs to be offered to the consumers. Chapter 7 explores how the blockchain approach can be employed to foster this REC market. The goal is to determine the design that should allow a DSO to accept peer-to-peer energy exchanges based on a distributed ledger supported by blockchain technology. To this end, an evaluation is conducted integrating several designs based on criteria such as acceptance of the wholesale/retail market, the resilience of the consensus to approve a block, the accuracy, traceability, privacy, and security of the proposed schemes. Chapter 8 poses that, despite its success and large use in other crypto-currencies, Proof of Work's disadvantages are high latency, a low transaction rate, and a high energy expenditure, making it a less-than-perfect choice for many applications. In addition, the validation of transactions is not carried out with a definite temporality. However, for certain use cases such as auctions or the exchange of energy in the REC context, there is a need for this temporality. The purpose of this article is to propose a new type of consensus that is faster, less energy-consuming and that can be synchronized with a time reference. The core of the reflection is the use of the Condorcet voting mechanism to determine the miner. The last chapter sets the main conclusion of this research. Two appendixes show other works conducted with fellow researchers during this PhD research journey.
Traditional power systems always rely on the fossil-fuel based power, the power is determined and dispatched in the centralized decision-making process. However, recent years have seen the increasing proliferation of distributed energy resources (DERs), communication, computing, and information devices in power systems, becoming next-generation autonomous power systems. On the one hand, the high penetration of DERs can provide a variety of benefits to next-generation autonomous power systems. For example, DERs can respond rapidly to near-term generation or reliability-related requirements, further improving their ability to enhance power system reliability and reduce costs. On the other hand, DERs have led to significant uncertainty and intermittency in power system controls and operations, especially for power system economic dispatch and voltage regulation problems. It becomes increasingly urgent to explore how to utilize DERs to improve power system efficiency, reliability, and resilience while mitigating the negative impacts of DERs on power systems. Traditional power system decision-making is the centrally-managed formulation and solution of system-wide optimizations. It always entails large amounts of computation time and information coming from customers, leading to customer privacy and scalability problems. Particularly, the capacity of each DER is always small, but the number of DERs in power systems is massive. Given such distribution characteristics of DERs, it might be impractical to apply traditional power system decision-making to autonomous power systems. To figure out this dilemma caused by DERs, it calls for new and innovative decision-making strategies to adapt to new characteristics of autonomous power systems: (1) The capacity of DERs is usually small, but the number of DERs is very massive. In addition, DERs are distributed across power systems. Coordinating massive DERs at different network locations is a big scalability challenge. (2) The uncertain and intermittent nature of DERs makes the operations of autonomous power systems more complicated, leading to different environmental change rates. Different environmental change rates might require different decision-making strategies. Offline decision-making strategies are suitable for slow environmental change rates since there is enough time for the algorithm convergence. In contrast, fast environmental change rates require online decision-making strategies to adjust the decision variables in real time. (3) The increasing deployment of communication, computing, and information devices, along with increasing data, will bring many opportunities and changes to autonomous power system decision-making. It has attracted increasing attention worldwide utilizing these devices and data to make better decisions for autonomous power systems. To this end, this work aims to propose scalable offline and online decision-making for next-generation power systems, utilizing DERs to enhance power system efficiency, reliability, and resilience. In particular, we focus on developing and designing offline and online decision-making to resolve a series of power system problems, including energy management, voltage regulation, and power flow problems. Chapters 2-3 mainly focus on the scalable offline power system decision-making, and Chapters 4-5 mainly focus on the scalable online power system decision-making. Chapter 2 develops a consensus-based transactive energy design managed by an Independent Distribution System Operator (IDSO) for an unbalanced distribution network. The network is populated by welfare-maximizing customers with price-sensitive and fixed loads who make multiple successive power decisions during each Operating Period (OP). The IDSO and customers engage in a negotiation process in advance of each OP to determine retail prices for OP that align customer power decisions with network constraints in a manner that preserves customer privacy. Convergence and optimality properties of this proposed design are established for an analytically formulated illustration: an unbalanced radial distribution network, populated by households, that is electrically connected to a relatively large regional transmission organization/independent system operator-managed transmission network. Chapter 3 aims to mitigate the voltage deviations and reduce the cost of supplying reactive power in distribution networks by optimally setting the reactive power of DERs. It proposes two types of Volt/VAr Control (VVC) strategies, including the hierarchical and decentralized VVC, based on a novel fast alternating direction method of multipliers (ADMM). For the fast ADMM-based hierarchical VVC strategy, it requires a central agent to iteratively communicate with local bus agents, but both the central agent and local bus agents can update variables in a closed form without solving sub-optimization problems. In contrast, the fast ADMM-based decentralized VVC strategy only requires the minimal information exchange between neighboring buses, but solving sub-optimization problems is necessary for local bus agents. Chapter 4 proposes an automatic self-adaptive local voltage control (ASALVC) by locally controlling VAr outputs of DERs. In this ASALVC strategy, each bus agent can locally and dynamically adjust its voltage droop function in accordance with time-varying system changes. The voltage droop function is associated with the bus-specific time-varying slope and intercept, which can be locally updated, merely based on local voltage measurements, without requiring communications. Stability, convergence and optimality properties of this local voltage control are analytically established. Numerical test cases are performed to validate and demonstrate the effectiveness and superiority of ASALVC. Chapter 5 proposes an online feedback-based linearized power flow model for unbalanced distribution networks with both wye-connected and delta-connected loads. The online feedback-based linearized model is grounded on the first-order Taylor expansion of the branch flow model, and updates the model parameters via online feedback by leveraging the instantaneous measurements of voltages and load consumption. Exploiting the connection structure of unbalanced radial distribution networks, we also provide a unified matrix-vector compact form of the model. Chapter 6 proposes an online voltage control strategy of DERs, based on the projected Newton method (PNM), for unbalanced distribution networks. The optimal VVC problem is formulated as an optimization program with the goal of maintaining the voltage profile across the network by coordinating the VAr outputs of DERs. To overcome the slow convergence rate of conventional gradient-based methods, a PNM-based VVC solution algorithm is developed to solve this problem. It utilizes a non-diagonal symmetric positive definite matrix, developed from the Hessian matrix of the objective, to scale the gradient, and thus a fast convergence performance can be expected in this Newton-like algorithm. Moreover, taking advantage of the instantaneous feedback of voltage measurements, the online implementation of the PNM-based voltage control is further designed to deal with fast system variations.