Data-driven coordinated voltage control method of distribution networks with high DG penetration

The highly penetrated distributed generators (DGs) aggravate the voltage violations in active distribution networks (ADNs). The coordination of various regulation devices such as on-load tap changers (OLTCs) and DG inverters can effectively address the voltage issues. Considering the problems of inaccurate network parameters and rapid DG fluctuation in practical operation, multi-source data can be utilized to establish the data-driven control model. In this paper, a data-driven coordinated voltage control method with the coordination of OLTC and DG inverters on multiple time-scales is proposed without relying on the accurate physical model. First, based on the multi-source data, a data-driven voltage control model is established. Multiple regulation devices such as OLTC and DG are coordinated on multiple time-scales to maintain voltages within the desired range. Then, a critical measurement selection method is proposed to guarantee the voltage control performance under the partial measurements in practical ADNs. Finally, the proposed method is validated on the modified IEEE 33-node and IEEE 123-node test cases. Case studies illustrate the effectiveness of the proposed method, as well as the adaptability to DG uncertainties

Low-frequency HVac transmission and distribution systems : planning and operation

The rapid load growth coupled with large-scale renewable generation sources remote from population centers demands future transmission grids to carry larger amounts of bulk power. For long distance transmission, high voltage direct current (HVdc) technology is superior to the conventional 50/60-Hz high-voltage alternating current (HVac) approach. Unfortunately, the lack of dc circuit breakers limits the application of HVdc technology to point-to point transmission links only. Thanks to advances in semiconductor materials and control methods, modern power converters make low-frequency HVac (LF-HVac) transmission systems possible. This new method of bulk power transmission overcomes the challenges in forming practical HVdc grids. Similarly, future distribution systems are expected to accommodate growing load demand in addition to increasing number of local inverter-based photovoltaic (PV) generations. Based on the aforementioned motivation, the objective of this dissertation is to develop power flow (PF) and optimal power flow (OPF) analysis methods for planning and operation of multi-frequency transmission systems that inherently employ a large number of converters. Similar steady-state analysis for a future distribution grid with high PV penetration, either as a standalone system or in coupling to a transmission grid, defines the second subject of the presented work. LF-HVac transmission scheme is recently proposed for long-distance bulk-power transmission by reducing the operating ac frequency to a low and variable value as determined by operational objectives and constraints. A multi-frequency HVac - HVdc system is formed by interconnecting conventional 50/60-Hz HVac grids to LF-HVac grids and HVdc lines. With respect to the first and major focus of this dissertation, a novel concept of an LFHVac grid employing converters with a centralized control is proposed. In addition, PF and OPF in a multi-frequency HVac - HVdc system are formulated by completely representing the steady-state models of HVac, HVdc, and LF-HVac grids as well as power converters, subject to all planning and operational constraints. The PF and OPF problems are solved by efficient algorithms based on the Newton-Raphson and predictor-corrector primal-dual interior-point methods (PCPDIPM), respectively. The proposed approach is applicable for a multi-frequency power system having arbitrary numbers of buses and topologies. Based on the PF results, the dependence of system MW losses on converter dispatch as well as the operating voltage and frequency in an LF-HVac is discussed and compared to that in HVdc transmission. On the other hand, the OPF analysis is applied to determine a suitable rated voltage in planning phase as well as optimal real-time operating frequency and dispatch of generators, shunt capacitors, and converters in operation phase. At the distribution side, high PV penetration might introduce voltage violations and reverse power flow. Besides the primary function of providing local generation, inverter-based PVs operating in grid-supporting mode can mitigate these consequences and minimize total losses with suitable dispatch. Therefore, the second focus of this work is to propose an exact OPF formulation and PCPDIPM-based solution algorithm to determine real-time dispatch of all inverters, switched capacitors, and voltage regulators with tap changers. The objective is to minimize total system losses, PV curtailment, and operations of capacitors and voltage regulators, in addition to elimination of voltage violations and reverse power flow. Effective computational strategies are proposed to allow real-time applications of the solution approach with a large number of constraints and variables. The accuracy and quality of the numerical solution in improving system performance are validated using practical distribution circuits with 15-minute load and PV data. High PV penetration also makes distribution systems more active and increases their impacts on the upstream transmission grids. As an extension of the work in distribution systems, a PF formulation as well as unified and sequential solution algorithms for joint transmission and distribution (T&D) systems are proposed, considering their physical coupling at substations. The potential effects of distributed PVs on transmission performance are also investigated. In addition, an OPF formulation and unified solution approach are proposed to determine the optimal operation for a joint T&D system. The objective is to minimize the total system losses while satisfying all operational constraints from both transmission and distribution sides.Electrical and Computer Engineerin

Deep Reinforcement Learning for the Optimization of Building Energy Control and Management

Most of the current game-theoretic demand-side management methods focus primarily on the scheduling of home appliances, and the related numerical experiments are analyzed under various scenarios to achieve the corresponding Nash-equilibrium (NE) and optimal results. However, not much work is conducted for academic or commercial buildings. The methods for optimizing academic-buildings are distinct from the optimal methods for home appliances. In my study, we address a novel methodology to control the operation of heating, ventilation, and air conditioning system (HVAC). We assume that each building in our campus is equipped with smart meter and communication system which is envisioned in the future smart grid. For academic and commercial buildings, HVAC systems consume considerable electrical energy and impact the personnels in the buildings which is interpreted as monetary value in this article. Therefore, we define social cost as the combination of energy expense and cost of human working productivity reduction. We implement game theory and formulate a controlling and scheduling game for HVAC system, where the players are the building managers and their strategies are the indoor temperature settings for the corresponding building. We use the University of Denver campus power system as the demonstration smart grid and it is assumed that the utility company can adopt the real-time pricing mechanism, which is demonstrated in this paper, to reflect the energy usage and power system condition in real time. For general scenarios, the global optimal results in terms of minimizing social costs can be reached at the Nash equilibrium of the formulated objective function. The proposed distributed HVAC controlling system requires each manager set the indoor temperature to the best response strategy to optimize their overall management. The building managers will be willing to participate in the proposed game to save energy cost while maintaining the indoor in comfortable zone. With the development of Artificial Intelligence and computer technologies, reinforcement learning (RL) can be implemented in multiple realistic scenarios and help people to solve thousands of real-world problems. Reinforcement Learning, which is considered as the art of future AI, builds the bridge between agents and environments through Markov Decision Chain or Neural Network and has seldom been used in power system. The art of RL is that once the simulator for a specific environment is built, the algorithm can keep learning from the environment. Therefore, RL is capable of dealing with constantly changing simulator inputs such as power demand, the condition of power system and outdoor temperature, etc. Compared with the existing distribution power system planning mechanisms and the related game theoretical methodologies, our proposed algorithm can plan and optimize the hourly energy usage, and have the ability to corporate with even shorter time window if needed. The combination of deep neural network and reinforcement learning rockets up the research of deep reinforcement learning, and this manuscript contributes to the research of power energy management by developing and implementing the deep reinforcement learning to control the HVAC systems in distribution power system. Simulation results prove that the proposed methodology can set the indoor temperature with respect to real-time pricing and the number of inside occupants, maintain indoor comfort, reduce individual building energy cost and the overall campus electricity charges. Compared with the traditional game theoretical methodology, the RL based gaming methodology can achieve the optiaml resutls much more quicker