With Power System being restructured in the vision of Smart Grid, it is important now more than ever to find suitable locations to place Distributed Generators (DG). Distributed generators, which may be renewable, are not limited to specific locations as in the case of conventional generators. Several papers have been published that make suggestions on where the optimal location of DG should be in a system. Objectives ranging from loss minimization to total cost minimization have been the factor for such studies. In this study, a new method is introduced that hopes to improve a current system in three ways by maximizing load, minimizing the locational marginal price and improving line contingency scenarios. The proposed methodology is simulated using MATPOWER’s Optimal Power Flow on the IEEE 14 bus test system.

abstract: The subject of this thesis is distribution level load management using a pricing signal in a Smart Grid infrastructure. The Smart Grid implements advanced meters, sensory devices and near real time communication between the elements of the system, including the distribution operator and the customer. A stated objective of the Smart Grid is to use sensory information to operate the electrical power grid more efficiently and cost effectively. One potential function of the Smart Grid is energy management at the distribution level, namely at the individual customer. The Smart Grid allows control of distribution level devices, including distributed energy storage and distributed generation, in operational real time. One method of load control uses an electric energy price as a control signal. The control is achieved through customer preference as the customer allows loads to respond to a dynamic pricing signal. In this thesis, a pricing signal is used to control loads for energy management at the distribution level. The model for the energy management system is created and analyzed in the z-domain due to the envisioned discrete time implementation. Test cases are used to illustrate stability and performance by analytic calculations using Mathcad and by simulation using Matlab Simulink. The envisioned control strategy is applied to the Future Renewable Electric Energy Distribution Management (FREEDM) system. The FREEDM system implements electronic (semiconductor) controls and therefore makes the proposed energy management feasible. The pricing control strategy is demonstrated to be an effective method of performing energy management in a distribution system. It is also shown that stability and near optimal response can be achieved by controlling the parameters of the system. Addition-ally, the communication bandwidth requirements for a pricing control signal are evaluated. / Dissertation/Thesis / M.S. Electrical Engineering 2013

abstract: Accurate forecasting of electricity prices has been a key factor for bidding strategies in the electricity markets. The increase in renewable generation due to large scale PV and wind deployment in California has led to an increase in day-ahead and real-time price volatility. This has also led to prices going negative due to the supply-demand imbalance caused by excess renewable generation during instances of low demand. This research focuses on applying machine learning models to analyze the impact of renewable generation on the hourly locational marginal prices (LMPs) for California Independent System Operator (CAISO). Historical data involving the load, renewable generation from solar and wind, fuel prices, aggregated generation outages is extracted and collected together in a dataset and used as features to train different machine learning models. Tree- based machine learning models such as Extra Trees, Gradient Boost, Extreme Gradient Boost (XGBoost) as well as models based on neural networks such as Long short term memory networks (LSTMs) are implemented for price forecasting. The focus is to capture the best relation between the features and the target LMP variable and determine the weight of every feature in determining the price. The impact of renewable generation on LMP forecasting is determined for several different days in 2018. It is seen that the prices are impacted significantly by solar and wind generation and it ranks second in terms of impact after the electric load. The results of this research propose a method to evaluate the impact of several parameters on the day-ahead price forecast and would be useful for the grid operators to evaluate the parameters that could significantly impact the day-ahead price prediction and which parameters with low impact could be ignored to avoid an error in the forecast. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2019

How to evaluate the performance of an electric power distribution system unambiguously and quantitatively is not easy. How to accurately measure the efficiency of it for a whole year, using real time hour-by-hour Locational Marginal Price data, is difficult. How to utilize distributed computing technology to accomplish these tasks with a timely fashion is challenging. This thesis addresses the issues mentioned above, by investigating feeder performance analysis of electric power distribution systems with distributed algorithm. Feeder performance analysis computes a modeled circuit's performance over an entire year, listing key circuit performance parameters such as efficiency, loading, losses, cost impact, power factor, three phase imbalance, capacity usage and others, providing detailed operating information for the system, and an overview of the performance of every circuit in the system. A diakoptics tearing method and Graph Trace Analysis based distributed computing technology is utilized to speed up the calculation. A general distributed computing architecture is established and a distributed computing algorithm is described. To the best of the author's knowledge, it is the first time that this detailed performance analysis is researched, developed and tested, using a diakoptics based tearing method and Graph Trace Analysis to split the system so that it can be analyzed with distributed computing technology. / Master of Science

The rapid growth of internet-connected smart devices capable of exchanging energy price information and adaptively controlling the consumption of connected loads, has paved the way for transactive control to make inroads in the modern grid. Transactive control frameworks integrate the wholesale and retail energy markets, and enable active participation of end users, thereby playing a key role in managing the rising number of distributed assets.However, the use of internet for the communication of data among the building, distribution,and transmission levels makes the system susceptible to external intrusions. A skilled adversary can potentially manipulate the exchanged data with the intention to inflict damage to the system or increase financial gains. In this thesis, the effect of such data integrity attacks on information exchanged between the distribution systems operator and end-users is investigated. Impact on grid operations is evaluated using different categories like operational, financial, user comfort and reliability parameters. It is shown that attack impact depends on a number of factors like attack duration, time of attack, penetration rate etc besides the attack magnitude. The effect of an attack continues to persist for some time after its removal and hence effective detection and mitigation strategies will be required to ensure system resilience and robustness. / Master of Science / Transactive energy is a framework where price-responsive loads adjust their energy consumption at a certain time according to the real-time energy price sent by the utility. Field demonstrations in recent years have shown that transactive control can effectively manage grid objectives and also monetarily benefit both the electric utility and end-users. Therefore, transactive energy is expected to make inroads into conventional grid operations in the next few years. As successful operation of such a market depends on the information exchanged among different stakeholders, a malicious adversary may try to inject false data and affect system operations. This thesis investigates how manipulating data in the transactive energy platform affects system operations and financial gains of different stakeholders. Understanding system behavior under attack conditions will help in formulating effective detection and mitigation strategies and enhancing system resilience.

Power systems all over the world advance towards the direction of deregulation in the past few years. Introducing competition mechanism and the principle of market rules in deregulation. Utility companies will face unprecedented changes and challenges. Taiwan power company is also working on the deregulation direction with a competitive environment opened up, it will improve the scientific and technological levels and the service quality of electricity. Load management functions as the marginal price of electricity is predicted. Consumers can get Real-Time Pricing information determine their own buying strategy. One most representative deregulation example in U.S.A. is the PJM(Pennsylvania¡BNew Jersey¡BMaryland)system combining generating, transmitting, distribution and sales of electricity. It offers the information of real-time power supply and is one of the cases in the world. Historical data in the thesis comes from PJM. Artificial Neural Network was designed to the Locational Marginal Price(LMP), considering the factors such as temperature and other relevant data from deregulation with the introduction of various parameters in forecasting, and the use of week as a counting base. LMP will be forecasted. The forecasted results will be to check the accuracy and performance with initial data.

In this thesis, methods of charging for the transmission system and optimising the expansion of the transmission network under the competitive power market are described. The first part of this thesis considers transmission tariff design. In the proposed approach, not only is all the necessary investment in the transmission system recovered, but also an absolute economic signal is offered which is very useful in the competitive power market. A fair power market opportunity is given to every participant by the new nodal-use method. The second part of this thesis considers transmission system expansion. All the tests are based on the Three Gorges Project in China. In this thesis, to optimally expand the transmission system, the LMP (Locational Marginal Price) selection method and the CBEP (Congestion-Based transmission system Expansion Planning) method are introduced. The LMP selection method is used to select optional plans for transmission system expansion. It is especially suitable for large transmission systems. The outstanding advantages of the LMP selection method are simplicity and computational efficiency. The CBEP method produces the optimal system expansion plan. For the first time, generation congestion and transmission congestion are separated within the system expansion problem. For this reason the CBEP method can be used in a supply-side power market and is suitable for the Chinese power market. In this thesis, the issue of how to relax the congestion in the transmission system have been solved. The transmission system can obtain enough income to recover the total required cost. For this reason more and more investment will come into the transmission system from investors. The risk for the independent generators is also under control in the CBEP method. Even when the system is congested, the uncertainty of LMP is taken into consideration.

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abstract: The smart grid initiative is the impetus behind changes that are expected to culminate into an enhanced distribution system with the communication and control infrastructure to support advanced distribution system applications and resources such as distributed generation, energy storage systems, and price responsive loads. This research proposes a distribution-class analog of the transmission LMP (DLMP) as an enabler of the advanced applications of the enhanced distribution system. The DLMP is envisioned as a control signal that can incentivize distribution system resources to behave optimally in a manner that benefits economic efficiency and system reliability and that can optimally couple the transmission and the distribution systems. The DLMP is calculated from a two-stage optimization problem; a transmission system OPF and a distribution system OPF. An iterative framework that ensures accurate representation of the distribution system's price sensitive resources for the transmission system problem and vice versa is developed and its convergence problem is discussed. As part of the DLMP calculation framework, a DCOPF formulation that endogenously captures the effect of real power losses is discussed. The formulation uses piecewise linear functions to approximate losses. This thesis explores, with theoretical proofs, the breakdown of the loss approximation technique when non-positive DLMPs/LMPs occur and discusses a mixed integer linear programming formulation that corrects the breakdown. The DLMP is numerically illustrated in traditional and enhanced distribution systems and its superiority to contemporary pricing mechanisms is demonstrated using price responsive loads. Results show that the impact of the inaccuracy of contemporary pricing schemes becomes significant as flexible resources increase. At high elasticity, aggregate load consumption deviated from the optimal consumption by up to about 45 percent when using a flat or time-of-use rate. Individual load consumption deviated by up to 25 percent when using a real-time price. The superiority of the DLMP is more pronounced when important distribution network conditions are not reflected by contemporary prices. The individual load consumption incentivized by the real-time price deviated by up to 90 percent from the optimal consumption in a congested distribution network. While the DLMP internalizes congestion management, the consumption incentivized by the real-time price caused overloads. / Dissertation/Thesis / M.S. Electrical Engineering 2013