Scalable offline and online decision-making for next-generation autonomous power systems

Abstract

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.