Optimized Household Demand Management with Local Solar PV Generation

Demand Side Management (DSM) strategies are of-ten associated with the objectives of smoothing the load curve and reducing peak load. Although the future of demand side manage-ment is technically dependent on remote and automatic control of residential loads, the end-users play a significant role by shifting the use of appliances to the off-peak hours when they are exposed to Day-ahead market price. This paper proposes an optimum so-lution to the problem of scheduling of household demand side management in the presence of PV generation under a set of tech-nical constraints such as dynamic electricity pricing and voltage deviation. The proposed solution is implemented based on the Clonal Selection Algorithm (CSA). This solution is evaluated through a set of scenarios and simulation results show that the proposed approach results in the reduction of electricity bills and the import of energy from the grid

Decentralized Demand Side Management with Rooftop PV in Residential Distribution Network

In the past extensive researches have been conducted on demand side management (DSM) program which aims at reducing peak loads and saving electricity cost. In this paper, we propose a framework to study decentralized household demand side management in a residential distribution network which consists of multiple smart homes with schedulable electrical appliances and some rooftop photovoltaic generation units. Each smart home makes individual appliance scheduling to optimize the electric energy cost according to the day-ahead forecast of electricity prices and its willingness for convenience sacrifice. Using the developed simulation model, we examine the performance of decentralized household DSM and study their impacts on the distribution network operation and renewable integration, in terms of utilization efficiency of rooftop PV generation, overall voltage deviation, real power loss, and possible reverse power flows.Comment: 5 pages, 7 figures, ISGT 2018 conferenc

Data-Enabled Distribution Grid Management

In 2020, U.S. electric utilities installed more than 94 million advanced meters, which brought the percentage of residential customers equipped with smart meters to 75%. This significant investment allows collecting extensive customer data at the distribution level, however, the data are not currently leveraged effectively to help with system operations. This dissertation aims to use the smart meters’ data to improve the grid’s reliability, stability, and controllability by solving two of the most challenging problems at the distribution level, namely distribution network phase identification and outage identification. Distribution networks have typically been the least observable and most dynamic and locally controlled elements in the power grid. Complete information about the network topology is continuously changing and is not always readily available when needed. Lack of phase connectivity information is a challenge, especially when rebalancing the grid and also in the aftermath of outages caused by extreme events. Traditionally, phase identification is executed manually. In this dissertation, a machine learning-based data mining method for accurate and efficient phase identification of residential customers is proposed by leveraging power consumption data collected through smart meters. The proposed method uses a high-pass filter to remove the redundant and irrelevant segments of the power consumption time series, and accordingly identifies the residential customers’ phase connectivity through a modified clustering algorithm. Accurate connectivity information among customers is essential for outage identification and management in distribution networks. Extreme weather events can cause significant damage to electric power grid infrastructure and lead to widespread power outages. The frequency and the intensity of these events are continuously increasing as a direct result of climate change. Identifying grid components that are damaged is the first step to recovering from extreme weather-related power outages. An effective data mining method in identifying distribution network line outages is presented in this dissertation by leveraging data collected through AMI. The line outage identification method is developed based on a Multi-Label Support Vector Machine (ML-SVM) classification scheme that utilizes the status of customers’ smart meters as input data and identifies the outage/operational status of distribution lines. Numerical simulations demonstrate the effectiveness of the proposed models and their respective viability in achieving the targeted operational objectives

Improving Grid Hosting Capacity and Inertia Response with High Penetration of Renewable Generation

To achieve a more sustainable supply of electricity, utilizing renewable energy resources is a promising solution. However, the inclusion of intermittent renewable energy resources in electric power systems, if not appropriately managed and controlled, will raise a new set of technical challenges in both voltage and frequency control and jeopardizes the reliability and stability of the power system, as one of the most critical infrastructures in the today’s world. This dissertation aims to answer how to achieve high penetration of renewable generations in the entire power system without jeopardizing its security and reliability. First, we tackle the data insufficiency in testing new methods and concepts in renewable generation integration and develop a toolkit to generate any number of synthetic power grids feathering the same properties of real power grids. Next, we focus on small-scale PV systems as the most growing renewable generation in distribution networks and develop a detailed impact assessment framework to examine its impacts on the system and provide installation scheme recommendations to improve the hosting capacity of PV systems in the distribution networks. Following, we examine smart homes with rooftop PV systems and propose a new demand side management algorithm to make the best use of distributed renewable energy. Finally, the findings in the aforementioned three parts have been incorporated to solve the challenge of inertia response and hosting capacity of renewables in transmission network

Aggregated DER Management in Advanced Distribution Grids

Evolution of modern power systems are more distinct in distribution grids, where the growing integration of microgrids as well as distributed energy resources (DERs), including renewable energy resources, electric vehicles (EVs), and energy storage, poses new challenges and opportunities to grid management and operation. Rapid growth of distribution automation as well as equipment monitoring technologies in the distribution grids further offer new opportunities for distribution asset management. The idea of aggregated DERs is proposed as a remedy to streamline management and operation of advanced distribution grids, as discussed under three subjects in this dissertation. The first subject matter focuses on DER aggregation in microgrid for distribution transformer asset management, while the second one stresses on aggregated DER for developing a spinning reserve-based optimal scheduling model of integrated microgrids. The aggregation of EV batteries in a battery swapping stations (BSS) for enhancing grid operation is investigated in the third subject. Distribution transformer, as the most critical component in the distribution grids, is selected as the component of the choice for asset management practices, where three asset management studies are proposed. First, an approach in estimating transformer lifetime is presented based on the IEEE Std. C57.91-2011 and using sensory data. Second, a methodology to obtain a low-error estimate of transformer loss-of-life is investigated, leveraging an integrated machine learning and data fusion technique. Finally, a microgrid-based distribution transformer asset management model is developed to prolong the transformer lifetime. The resulting model aims at reshaping the distribution transformer loading via aggregating microgrid DERs in an efficient and asset management-aware manner. The increasing penetration of microgrids in distribution grids sets the stage for the formation of multiple microgrids in an integrated fashion. Accordingly, a spinning reserved based optimal scheduling model for integrated microgrids is proposed to minimize not only the operation cost associated with all microgrids in the grid-connected operation, but also the costs of power deficiency and spinning reserve in the islanded operation mode. The resulting model aims at determining an optimal configuration of the system in the islanded operation, i.e., optimal super-holons combination, which plays a key role in minimizing the system-aggregated operation cost and improving the overall system reliability. The evolving distribution grids introduce the concept of the BSS, which is emerging as a viable means for fast energy refill of EVs, to offer energy and ancillary services to the distribution grids through DER aggregation. Using a mixed-integer linear programming method, an uncertainty-constrained BSS optimal operation model is presented that not only covers the random customer demands of fully charged batteries, but also focuses on aggregating the available distributed batteries in the BSS to reduce its operation cost. Furthermore, the BSS is introduced as an energy storage for mitigating solar photovoltaic (PV) output fluctuations, where the distributed batteries in the BSS are modeled as an aggregated energy storage to capture solar generation variability. Numerical simulations demonstrate the effectiveness of the proposed models as well as their respective viability in achieving the predefined operational objectives