A Response-Function-Based Coordination Method for Transmission-Distribution-Coupled AC OPF

With distributed generation highly integrated into the grid, the transmission-distribution-coupled AC OPF (TDOPF) becomes increasingly important. This paper proposes a response-function-based coordination method to solve the TDOPF. Different from typical decomposition methods, this method employs approximate response functions of the power injections with respect to the bus voltage magnitude in the transmission-distribution (T-D) interface to reflect the "reaction" of the distribution to the transmission system control. By using the response functions, only one or two iterations between the transmission system operator (TSO) and the distribution system operator(s) (DSO(s)) are required to attain a nearly optimal TDOPF solution. Numerical tests confirm that, relative to a typical decomposition method, the proposed method does not only enjoy a cheaper computational cost but is workable even when the objectives of the TSO and the DSO(s) are in distinct scales.Comment: This paper will appear at 2018 IEEE PES Transmission and Distribution Conference and Expositio

Learning the price response of active distribution networks for TSO-DSO coordination

The increase in distributed energy resources and flexible electricity consumers has turned TSO-DSO coordination strategies into a challenging problem. Existing decomposition/decentralized methods apply divide-and-conquer strategies to trim down the computational burden of this complex problem, but rely on access to proprietary information or fail-safe real-time communication infrastructures. To overcome these drawbacks, we propose in this paper a TSO-DSO coordination strategy that only needs a series of observations of the nodal price and the power intake at the substations connecting the transmission and distribution networks. Using this information, we learn the price response of active distribution networks (DN) using a decreasing step-wise function that can also adapt to some contextual information. The learning task can be carried out in a computationally efficient manner and the curve it produces can be interpreted as a market bid, thus averting the need to revise the current operational procedures for the transmission network. Inaccuracies derived from the learning task may lead to suboptimal decisions. However, results from a realistic case study show that the proposed methodology yields operating decisions very close to those obtained by a fully centralized coordination of transmission and distribution

Data Driven Synthetic Load Modeling for Smart City Energy Management Studies

The primary aim of this dissertation is to provide synthetic residential load models with granular level information on the customers having information about the appliances that constitute each individual residential customer through time. The synthetic load model is capable of being widely utilized by the power system research community since only publicly available data is utilized for its generation. This gives researcher’s access to how the synthetic load was made and also how accurate the model is in representing real power system regions. As the title of the dissertation suggests, the synthetic residential load models are intended for smart city energy management studies. Smart city energy management studies have the ability to control tens of thousands of electricity customers in a coordinated manner to enact system-wide electric load changes. Such load changes have the potential to reduce congestion (i.e. stress on power system components) and peak demand (i.e. the need for peaking generation), among other benefits. For smart city energy management studies to have the capability of evaluating how their strategies would impact the actual power system, datasets that accurately characterize the system load are required that also contain individual loads of all buildings in a given area. Currently, such data is publicly unavailable due to privacy concerns. This dissertation’s synthetic residential load model combines a top down and bottom up approach for modeling individual residential customers and their individual electric assets, each possessing their own characteristics, using time-varying queueing models. The aggregation of all customer loads created by the queueing models represents a known city-sized load curve to be used in smart city energy management studies. The dissertation presents three queueing residential load models that make use of only publicly available data to alleviate privacy concerns. The proposed approach is mainly driven by the aggregated distribution companies load. An open-source Python tool to allow researchers to generate residential load data for their studies is also provided. The simulation results comparing the three queueing synthetic load models consider the ComEd region (utility company from Chicago, IL) to demonstrate the model’s characteristics, impact of the choice of model parameters, and scalability performance of the Python tool. The developed residential synthetic queueing load models are utilized to create the Midwest 240-Node distribution test case system, which generates appliance-level synthetic residential load for 1,120 homes for the Iowa State distribution system test case with 193 load nodes over three feeders. The Midwest 240-Node is a real distribution system from the Midwest region of the U.S. with real one-year smart meter data at the hourly aggregated node level resolution for 2017 available in an OpenDSS model. The synthetic residential queueing load model generated for the Midwest 240-Node one-year date has a mean absolute percentage error of 2.5828% in relation to the real smart meter data. The Midwest 240-Node distribution system OpenDSS model was converted to GridLAB-D to enable smart grid and transactive energy studies. The percentage of maximum error observed on voltage magnitude from the OpenDSS to GridLAB-D model is below 0.0009%. The GridLAB-D model and the generated synthetic residential load is made publicly available. The Midwest 240-Node real distribution system with the synthetic residential load that follows the real data from smart meters is intended to be a distributed energy active consumer test system network. The focus of the developed synthetic residential load models is smart city energy management studies; however, they can be utilized in many power systems studies to evaluate economic and technical impacts of distributed energy resources. For example, this dissertation also presents the utilization of the synthetic models for a PV rich low voltage network. The main component of the smart grid is demand response. Demand response, or energy management, utilizes commonly passive load in to active power system resources. Residential demand response, when aggregated, is capable of performing system-wide changes that enable its participation in the power system markets. This dissertation developed residential synthetic models to enable the standardization of approaches and allow different approaches to be compared under the same environment

Using artificial intelligence to support emerging networks management approaches

In emergent networks such as Internet of Things (IoT) and 5G applications, network traffic estimation is of great importance to forecast impacts on resource allocation that can influence the quality of service. Besides, controlling the network delay caused with route selection is still a notable challenge, owing to the high mobility of the devices. To analyse the trade-off between traffic forecasting accuracy and the complexity of artificial intelligence models used in this scenario, this work first evaluates the behavior of several traffic load forecasting models in a resource sharing environment. Moreover, in order to alleviate the routing problem in highly dynamic ad-hoc networks, this work also proposes a machine-learning-based routing scheme to reduce network delay in the high-mobility scenarios of flying ad-hoc networks, entitled Q-FANET. The performance of this new algorithm is compared with other methods using the WSNet simulator. With the obtained complexity analysis and the performed simulations, on one hand the best traffic load forecast model can be chosen, and on the other, the proposed routing solution presents lower delay, higher packet delivery ratio and lower jitter in highly dynamic networks than existing state-of-art methods

Symmetry in Renewable Energy and Power Systems

This book includes original research papers related to renewable energy and power systems in which theoretical or practical issues of symmetry are considered. The book includes contributions on voltage stability analysis in DC networks, optimal dispatch of islanded microgrid systems, reactive power compensation, direct power compensation, optimal location and sizing of photovoltaic sources in DC networks, layout of parabolic trough solar collectors, topologic analysis of high-voltage transmission grids, geometric algebra and power systems, filter design for harmonic current compensation. The contributions included in this book describe the state of the art in this field and shed light on the possibilities that the study of symmetry has in power grids and renewable energy systems