With proliferation of distributed energy resources (DERs) and advent of advanced measurement devices in modern distribution grids, an advanced distribution management system (ADMS) is needed to be developed in order to maintain reliability and efficiency of modern distribution systems. However, the numbers of sensors and measurement devices in distribution networks are limited, and communication links between switch devices, sensors, and ADMS are not well-established. Moreover, the fast voltage fluctuation and violation issues caused by high penetration levels of DERs cannot be easily coped with traditional Volt-VAr control (VVC) devices. In this regard, this Dissertation report proposes an ADMS tool including all core components, i.e., topology processor, state estimation, outage detection, DERs scheduling, and Volt-VAr optimization of DERs, for smart distribution networks with DERs, smart meters, and micro-phasor measurement units (micro-PMUs). In order to execute the ADMS tool’s components in an unbalanced distribution system, novel nonlinear and convex AC optimal power flow models based on current-voltage (IVACOPF) formulation are proposed for an unbalanced distribution system with DERs. Applications of the proposed convex IVACOPF model on key parts of ADMS and DERs management system (DERMS), i.e., (i) simultaneous state estimation, topology processor, and outage detection, (ii) DERs scheduling, and (iii) Volt-VAr optimization of DERs, are presented in this report. In this regard, an efficient MIQP-based optimization model based on IVACOPF is proposed to simultaneously identify real-time network topology, estimate system state, and detect outages of unbalanced distribution systems. The proposed model copes with challenges of a real distribution network including: (1) limited locations of measurement devices and unsynchronized measurement data as well as missing and bad data, and (2) complicated mixed-phase switch actions and mutual impedances and shunt admittances. For the Volt-VAr optimization component of ADMS and DERs scheduling, an operational scheduling model of DERs and PV smart inverters with Volt-VAr controllers is proposed using IVACOPF in an unbalanced distribution network. The setpoints of controller setting of each individual PV smart inverter are optimized within the allowable range of the IEEE 1547-2018 standard to improve local as well as system-level voltage regulation in an unbalanced distribution system.

Energy market participants' optimal bidding strategies and profit maximization is built upon accurate locational marginal price (LMP) predictions. In wholesale electricity markets, LMPs are strongly spatio-temporal correlated. Without access to confidential information on system topology, model parameters, or market operation conditions, market participants can only accept data-driven methods to utilize publicly available market data to predict LMPs. Most previous data-driven studies on LMP forecasting only leveraged temporal correlations among historical LMPs, and very few of them learned the spatial correlations to improve forecasting accuracy. In this dissertation, unsupervised data-driven approaches are proposed to predict LMPs in real-world energy markets from market participants' perspective. To take advantage of the spatio-temporal correlations, a general data structure is introduced to organize system-wide heterogeneous market data streams into the format of market data 2-dimensional (2D) arrays and 3-dimensional (3D) tensors. The system-wide LMP prediction problem is formulated as a sequence prediction problem. A generative adversarial network (GAN) based prediction model is adopted to learn the spatio-temporal correlations among historical LMPs preserved in the market data 3D tensors, then predict future system-wide LMPs. Multi-loss functions are introduced to assist the adversarial training procedure. A convolutional long-short-term memory (CLSTM)-based GAN is developed to improve forecasting accuracy. All LMP price components are jointly determined by the interactions between the market clearing process and the generator bidding process. The market participants’ LMP forecasting problem can be formulated as a sequential decision-making model considering the interactive market clearing and generation bidding decision-making processes. The spatio-temporal decision transformer is proposed to learn the underlying sequential decision-making model from historical spatio-temporal market data and forecast LMPs as the future actions of these interactive decision-making processes. A two-stage approach is proposed to incorporate historical generation bids into energy price prediction from market participants' perspective. Historical generation bids are taken as the first stage's output and the second stage's input in the training process. The implicit correlation among locational bids, demands, and energy prices is learned to improve price forecasting accuracy. The proposed approaches are verified through case studies using both real-world and simulated data.