DISTRIBUTION SYSTEM OPTIMIZATION WITH INTEGRATED DISTRIBUTED GENERATION

In this dissertation, several volt-var optimization methods have been proposed to improve the expected performance of the distribution system using distributed renewable energy sources and conventional volt-var control equipment: photovoltaic inverter reactive power control for chance-constrained distribution system performance optimisation, integrated distribution system optimization using a chance-constrained formulation, integrated control of distribution system equipment and distributed generation inverters, and coordination of PV inverters and voltage regulators considering generation correlation and voltage quality constraints for loss minimization. Distributed generation sources (DGs) have important benefits, including the use of renewable resources, increased customer participation, and decreased losses. However, as the penetration level of DGs increases, the technical challenges of integrating these resources into the power system increase as well. One such challenge is the rapid variation of voltages along distribution feeders in response to DG output fluctuations, and the traditional volt-var control equipment and inverter-based DG can be used to address this challenge. These methods aim to achieve an optimal expected performance with respect to the figure of merit of interest to the distribution system operator while maintaining appropriate system voltage magnitudes and considering the uncertainty of DG power injections. The first method is used to optimize only the reactive power output of DGs to improve system performance (e.g., operating profit) and compensate for variations in active power injection while maintaining appropriate system voltage magnitudes and considering the uncertainty of DG power injections over the interval of interest. The second method proposes an integrated volt-var control based on a control action ahead of time to find the optimal voltage regulation tap settings and inverter reactive control parameters to improve the expected system performance (e.g., operating profit) while keeping the voltages across the system within specified ranges and considering the uncertainty of DG power injections over the interval of interest. In the third method, an integrated control strategy is formulated for the coordinated control of both distribution system equipment and inverter-based DG. This control strategy combines the use of inverter reactive power capability with the operation of voltage regulators to improve the expected value of the desired figure of merit (e.g., system losses) while maintaining appropriate system voltage magnitudes. The fourth method proposes a coordinated control strategy of voltage and reactive power control equipment to improve the expected system performance (e.g., system losses and voltage profiles) while considering the spatial correlation among the DGs and keeping voltage magnitudes within permissible limits, by formulating chance constraints on the voltage magnitude and considering the uncertainty of PV power injections over the interval of interest. The proposed methods require infrequent communication with the distribution system operator and base their decisions on short-term forecasts (i.e., the first and second methods) and long-term forecasts (i.e., the third and fourth methods). The proposed methods achieve the best set of control actions for all voltage and reactive power control equipment to improve the expected value of the figure of merit proposed in this dissertation without violating any of the operating constraints. The proposed methods are validated using the IEEE 123-node radial distribution test feeder

Wireless sensors and IoT platform for intelligent HVAC control

Energy consumption of buildings (residential and non-residential) represents approximately 40% of total world electricity consumption, with half of this energy consumed by HVAC systems. Model-Based Predictive Control (MBPC) is perhaps the technique most often proposed for HVAC control, since it offers an enormous potential for energy savings. Despite the large number of papers on this topic during the last few years, there are only a few reported applications of the use of MBPC for existing buildings, under normal occupancy conditions and, to the best of our knowledge, no commercial solution yet. A marketable solution has been recently presented by the authors, coined the IMBPC HVAC system. This paper describes the design, prototyping and validation of two components of this integrated system, the Self-Powered Wireless Sensors and the IOT platform developed. Results for the use of IMBPC in a real building under normal occupation demonstrate savings in the electricity bill while maintaining thermal comfort during the whole occupation schedule.QREN SIDT [38798]; Portuguese Foundation for Science & Technology, through IDMEC, under LAETA [ID/EMS/50022/2013

Coordinated Voltage and Reactive Power Control for Renewable Dominant Smart Distribution Systems

Driven by their economic and environmental advantages, smart grids promote the deployment of active components, including renewable energy sources (RESs), energy storage systems (ESSs), and electric vehicles (EVs), for sustainability and environmental benefits. As a result of smart grid technologies and the amount of data collected by smart meters, better operation and control schemes can be developed to allow for cleaner energy with high efficiency, and without breaching network operating constraints. Power distribution networks may face some operational and control challenges as the integration of intermittent energy sources (wind and PV power systems) increases. Some of these challenges include voltage rise and fluctuation, reverse power flow, and the malfunction of conventional Volt/Var control devices. Depending on their location, RESs may introduce two issues related to the Volt/Var control problem, the first of which is that the severity of loading variations will be greater than the case without RESs. The second occurs when the RES is connected between the load center and any regulating devices. The power in-feed from the intermittent RESs may not only mislead the regulator’s control circuit, resulting in unfavorable voltage, but may also enforce the regulator taps to operate randomly following bus voltage variations. This thesis investigates and presents a methodology for the Volt/Var control problem in Smart Distribution Grids (SDGs) under the high penetration and fluctuation of RESs. The research involves the application of predictive control actions to optimally set Volt/Var control devices before the predicted voltage violation takes place. The main objective of this controller is to manage and control the operation of Volt/Var devices in an optimal way that improves the voltage profile along the feeders, reduces real power losses and minimizes the number of Volt/Var device taps and/or switching movements under all loading conditions and for high penetration RESs. This thesis first presents a very Short-Term Stacking Ensemble (STSE) forecasting model for solar PV and wind power outputs that is developed to predict the generated power for intervals of 15 minutes. The proposed model combines heterogeneous machine learning algorithms composed of three well-established models: Support Vector Regression (SVR); Radial Basis Function Neural Network (RBFNN); and Random Forest (RF) heuristically via SVR. The STSE model aims to minimize the prediction error associated with renewable resources when used in the real-time operation of power distribution networks. Secondly, a day-ahead Predictive Volt/Var Control (PVVC) model is developed to find the optimal coordination between Volt/Var control devices under the high penetration and power variations of RESs. The objective of the PVVC model is defined as simultaneous minimization of voltage deviation at each bus, power losses, operating cycle of regulation equipment, and RES curtailment. The benefit of using smart inverter interface RESs with the capability of injective/absorbing reactive power is examined and applied as ancillary services for voltage support. Thirdly, a Sequential Predictive Control (SPC) Strategy for smart grids is developed. The model uses the past and currently available data to forecast demand and RES outputs for intervals of 15 minutes, with real-time updating mechanisms. It then schedules the settings and operations of Volt/Var control devices by solving the Volt/Var control problem in a rolling horizon optimization framework. Because the optimization must be solved in a short interval with a global solution, a solution methodology for linearizing the nonlinear optimization problem is adapted. The original control problem, which is a Mixed-Integer Nonlinear Programming (MINLP) optimization problem, is transformed into a Mixed Integer Second Order Conic Programming (MISOCP) problem that guarantees a global solution through convexity and remarkably reduces the computational burden. Case studies carried out to compare the proposed model against state-of-the-art models provides evidence for the proposed model’s effectiveness. Results indicate that the SPC is capable of accurately solving the control problem within small time slots. The proposed models aim to efficiently operate SDGs at a high penetration level of RES for a day-ahead, as well as in real-time, depending on the preference of network operators. The primary purpose is to minimize operating costs while increasing the efficiency and lifespan of Volt/Var control devices