A Reserve-Based Method for Mitigating the Impact of Renewable Energy

The fundamental operating paradigm of today\u27s power systems is undergoing a significant shift. This is partially motivated by the increased desire for incorporating variable renewable energy resources into generation portfolios. While these generating technologies offer clean energy at zero marginal cost, i.e. no fuel costs, they also offer unique operating challenges for system operators. Perhaps the biggest operating challenge these resources introduce is accommodating their intermittent fuel source availability. For this reason, these generators increase the system-wide variability and uncertainty. As a result, system operators are revisiting traditional operating strategies to more efficiently incorporate these generation resources to maximize the benefit they provide while minimizing the challenges they introduce. One way system operators have accounted for system variability and uncertainty is through the use of operating reserves. Operating reserves can be simplified as excess capacity kept online during real time operations to help accommodate unforeseen fluctuations in demand. With new generation resources, a new class of operating reserves has emerged that is generally known as flexibility, or ramping, reserves. This new reserve class is meant to better position systems to mitigate severe ramping in the net load profile. The best way to define this new requirement is still under investigation. Typical requirement definitions focus on the additional uncertainty introduced by variable generation and there is room for improvement regarding explicit consideration for the variability they introduce. An exogenous reserve modification method is introduced in this report that can improve system reliability with minimal impacts on total system wide production costs. Another potential solution to this problem is to formulate the problem as a stochastic programming problem. The unit commitment and economic dispatch problems are typically formulated as deterministic problems due to fast solution times and the solutions being sufficient for operations. Improvements in technical computing hardware have reignited interest in stochastic modeling. The variability of wind and solar naturally lends itself to stochastic modeling. The use of explicit reserve requirements in stochastic models is an area of interest for power system researchers. This report introduces a new reserve modification implementation based on previous results to be used in a stochastic modeling framework. With technological improvements in distributed generation technologies, microgrids are currently being researched and implemented. Microgrids are small power systems that have the ability to serve their demand with their own generation resources and may have a connection to a larger power system. As battery technologies improve, they are becoming a more viable option in these distributed power systems and research is necessary to determine the most efficient way to utilize them. This report will investigate several unique operating strategies for batteries in small power systems and analyze their benefits. These new operating strategies will help reduce operating costs and improve system reliability

Integrated Generation Management for Maximizing Renewable Resource Utilization

Two proposed methods to reduce the effective intermittency and improve the efficiency of wind power generation in the grid are spatial smoothing of wind generation and utilization of short term electrical storage to deal with lulls in production. In this thesis, based on a concept called integrated generation management (IGM), we explore the impact of spatial smoothing and the use of emerging plug-in hybrid electric vehicles (PHEVs) as a potential storage resource to the smart-grid. IGM combines nuclear, slow load-following coal, fast load-following natural gas, and renewable wind generation with an optimal control method to maximize the renewable generation and minimize the fossil generation. With the increasing penetration of PHEVs, the power grid is seeing new opportunities to make itself smarter than ever by utilizing those relatively large batteries. Based on current projections of PHEV market penetration and various wind generation scenarios, we demonstrate the potential for efficient wind integration at levels of approaching 30% of the aver- age electrical load with utilization efficiency exceeding 65%. At lower levels of integration (e.g. 15%), efficiencies are possible exceeding 85%

Power System Steady-State Analysis with Large-Scale Electric Vehicle Integration

It is projected that the electric vehicle will become a dominant method of transportation within future road infrastructure. Moreover, the electric vehicle is expected to form an additional role in power systems in terms of electrical storage and load balancing. This paper considers the latter role of the electric vehicle and its impact on the steady-state stability of power systems, particularly in the context of large-scale electric vehicle integration. The paper establishes a model framework which examines four major issues: electric vehicle capacity forecasting; optimization of an object function; electric vehicle station siting and sizing; and steady-state stability. A numerical study has been included which uses projected United Kingdom 2020 power system data with results which indicate that the electric vehicle capacity forecasting model proposed in this paper is effective to describe electric vehicle charging and discharging profiles. The proposed model is used to establish criteria for electric vehicle station siting and sizing and to determine steady-state stability using a real model of a small-scale city power system

Grid integration of intermittent renewable energy sources using price-responsive plug-in electric vehicles

Plug-in electric vehicles (PEVs) are expected to balance the fluctuation of re-newable energy sources (RES). To investigate the contribution of PEVs, the availability of mobile battery storage and the control mechanism for load man-agement are crucial. This study therefore combined the following: a stochastic model to determine mobility behavior, an optimization model to minimize vehicle charging costs and an agent-based electricity market equilibrium model to esti-mate variable electricity prices. The variable electricity prices are calculated based on marginal generation costs. Hence, because of the merit order effect, the electricity prices provide incentives to consume electricity when the supply of renewable generation is high. Depending on the price signals and mobility behavior, PEVs calculate a cost minimizing charging schedule and therefore balance the fluctuation of RES. The analysis shows that it is possible to limit the peak load using the applied control mechanism. The contribution of PEVs to improving the integration of intermittent renewable power generation into the grid depends on the characteristic of the RES generation profile. For the Ger-man 2030 scenario used here, the negative residual load was reduced by 15 to 22 percent and the additional consumption of negative residual load was be-tween 34 and 52 percent. --Plug-in electric vehicles,demand-side management,variable prices,intermittent generation

A review on the virtual power plant: Components and operation systems

© 2016 IEEE. Due to the high penetration of Distributed Generations (DGs) in the network and the presently involving competition in all electrical energy markets, Virtual Power Plant (VPP) as a new concept has come into view, with the intention of dealing with the increasing number of DGs in the system and handling effectively the competition in the electricity markets. This paper reviews the VPP in terms of components and operation systems. VPP fundamentally is composed of a number of DGs including conventional dispatchable power plants and intermittent generating units along with possible flexible loads and storage units. In this paper, these components are described in an all-inclusive manner, and some of the most important ones are pointed out. In addition, the most important anticipated outcomes of the two types of VPP, Commercial VPP (CVPP) and Technical VPP (TVPP), are presented in detail. Furthermore, the important literature associated with Combined Heat and Power (CHP) based VPP, VPP components and modeling, VPP with Demand Response (DR), VPP bidding strategy, and participation of VPP in electricity markets are briefly classified and discussed in this paper

Optimization Techniques for Modern Power Systems Planning, Operation and Control

Recent developments in computing, communication and improvements in optimization techniques have piqued interest in improving the current operational practices and in addressing the challenges of future power grids. This dissertation leverages these new developments for improved quasi-static analysis of power systems for applications in power system planning, operation and control. The premise of much of the work presented in this dissertation centers around development of better mathematical modeling for optimization problems which are then used to solve current and future challenges of power grid. To this end, the models developed in this research work contributes to the area of renewable integration, demand response, power grid resilience and constrained contiguous and non-contiguous partitioning of power networks. The emphasis of this dissertation is on finding solutions to system operator level problems in real-time. For instance, multi-period mixed integer linear programming problem for applications in demand response schemes involving more than million variables are solved to optimality in less than 20 seconds of computation time through tighter formulation. A balanced, constrained, contiguous partitioning scheme capable of partitioning 20,000 bus power system in under one minute is developed for use in time sensitive application area such as controlled islanding