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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 networks
Enhancing the integration of renewable power generation from wind and solar into the traditional power network requires the mitigation of the vulnerabilities affecting the grid as a result of the intermittent nature of these resources. Variability and ramp events in power output are the key challenges to the system operators due to their impact on system balancing, reserves management, scheduling, and commitment of generation units. This book presents development of energy management system for renewable power generation (EMSRPG) tool that aims to achieve power-dispatching strategies based on forecasting renewable energy resources outputs to guarantee optimal dispatch of hybrid wind-solar photovoltaic power systems (HWSPS). The key selling points of the book include the following: Renewable energy management in modern and future smart power systems Energy management systems Modeling and simulations using a real-time digital simulator (RTDS) High penetration level of renewable energy sources Case studies based on Oman’s power systems and other power grids This book discusses the challenges of integrating renewable resources, including low inertia systems, hosting capacity limitations of existing power systems, and weak grids. It further examines the detailed topologies, operation principles, recent developments in control techniques, and stability of power systems with a large scale of renewables. Finally, it presents case studies of recent projects from around the world where dispatchable power plant techniques are used to enhance power system operation.
Electricity generation accounts for nearly half of the total CO2 emissions in the United States. For this reason, the development and integration of renewable resources will play an essential role in achieving the societal objective of mitigating climate change through reduction of greenhouse gas emissions. In conjunction with the environmental benefits of renewable energy, the most common renewable sources, such as wind and solar, also increase the uncertainties surrounding generation in power systems, which adds significant challenges to the system operation and planning. The uncertainties and forecasting errors surrounding renewable generation are normally addressed through the use of reserves from traditional generators. At greater levels of renewable penetration, sufficient generator reserves may not be available or economically viable. In contrast, the promise of demand-side resources in this arena lies in the spatially widespread availability, rapid response potential, and lower cost of features that already exist in the system. However, the challenge of responsive demand arises from the need to understand, manage, and incentivize a very large number of resources to participate effectively in efficient operation of the complex power system. Since demand response comes from the distribution system, microgrids as the basic building blocks of future distribution systems will be a critical environment for the study of demand response. To support integration of microgrids with flexible loads in future power systems, the operational mode of power systems will need to evolve. Therefore, it is going to be critical to have new and efficient co-optimization methods for coordination of the various power market participants and the scheduling of resources in the power systems of the future. Motivated by the rapid increase in renewable penetration, the need for effective demand response programs, and a changing system structure, this dissertation seeks to define a new strategy that supports co-optimization of various participants in power systems with emphasis on high renewable penetration and demand response. This strategy has three components; 1) an exploration of the capabilities of different types of demand response programs in a microgrid, 2) development and implementation of a bi-level framework for co-optimization of the main grid with high renewable penetration and a microgrid with demand response capabilities, and 3) expansion of the bi-level framework from a microgrid to a general distribution system to explore the advantages of the bi-level co-optimization approach over the traditional optimization approach. Conclusions of this work illustrate that the stochastic rolling horizon approach could effectively manage the operation of a microgrid with various demand response programs. In addition, the bi-level approach is a promising co-optimization framework for the transmission and distribution levels that could increase system renewable penetration and reduce operation costs. Compared to the traditional framework, the bi-level framework yields more equitable cost sharing patterns among the market participants as well as better support for the power system evolution.
This book makes the area of integration of renewable energy into the existing electricity grid accessible to engineers and researchers. This is a self-contained text which has models of power system devices and control theory necessary to understand and tune controllers in use currently. The new research in renewable energy integration is put into perspective by comparing the change in the system dynamics as compared to the traditional electricity grid. The emergence of the voltage stability problem is motivated by extensive examples. Various methods to mitigate this problem are discussed bringing out their merits clearly. As a solution to the voltage stability problem, the book covers the use of FACTS devices and basic control methods. An important contribution of this book is to introduce advanced control methods for voltage stability. It covers the application of output feedback methods with a special emphasis on how to bound modelling uncertainties and the use of robust control theory to design controllers for practical power systems. Special emphasis is given to designing controllers for FACTS devices to improve low-voltage ride-through capability of induction generators. As generally PV is connected in low voltage distribution area, this book also provides a systematic control design for the PV unit in distribution systems. The theory is amply illustrated with large IEEE Test systems with multiple generators and dynamic load. Controllers are designed using Matlab and tested using full system models in PSSE.
The rapid growth of electricity generation from variable renewable resources like wind and solar has greatly impacted wholesale energy markets and raised questions about future grid stability. With this paradigm shift, some existing coal, natural gas, and nuclear generators have encountered financial struggles, which has led to widespread retirements and tight capacity margins in some regions. Although this change could lead to reduced carbon emissions, synchronous generators provide some important reliability benefits to the grid that other technologies cannot easily replace. To assess the impact of an energy transition away from synchronous generation (e.g. fossil fuel fired power plants) and towards non-synchronous generation (e.g. wind and solar), future grid stability was investigated in the following three studies: (1) evaluating rotational inertia as a component of grid reliability with high penetrations of variable renewable energy, (2) determining the impact of non-synchronous generation on grid stability and identifying mitigation pathways, and (3) quantifying the regional economic and stability impacts of grid-scale energy storage. First, a method was developed to assess grid stability with increasing penetrations of non-synchronous renewable energy generation to determine when an electric grid might be more vulnerable to frequency contingencies, such as a generator outage. Unit commitment and dispatch modeling was used to quantify system inertia, an established proxy for grid stability. A case study of the Electric Reliability Council of Texas grid was used to illustrate the method. Results from the modeled scenarios showed that the Texas grid is resilient to major grid changes, even with relatively high penetrations (~30% of annual energy generation compared to 19% in 2018) of renewable energy. However, retiring nuclear power plants and private-use networks in the model led to unstable inertia levels in our results. When the system inertia was constrained to meet a minimum threshold in our model, multiple coal and natural gas combined-cycle plants were dispatched at part-load or at their minimum operating level to maintain stable system inertia levels. This behavior is expected to expand with higher renewable energy penetrations and could occur on other electric grids that are reliant on synchronous generators for inertia support. A method was also developed for assessing the impacts of stability support from inverter-connected resources. In this analysis, a fully disaggregated, inertia-constrained unit commitment and dispatch model was used to study the stability of future grid scenarios with high penetrations of non-synchronous renewable energy generation. As before, the Texas grid (the Electric Reliability Council of Texas – ERCOT) was used as a test case and instances when the system inertia fell below 100 GW·s (the grid's current minimum level) were found, starting at an annual renewable energy penetration (including both synchronous and non-synchronous renewable resources) of ~30% in our model. At an ~88% renewable energy penetration, the average system inertia level also fell to 100 GW·s. When the modeled critical inertia limit was reduced to 80 GW·s, no critical inertia hours occurred for renewable energy penetrations up to 93% of annual energy. The critical inertia limit could drop to 60 GW·s if the largest generators in ERCOT (two co-located nuclear plants) were retired, but this had the same effect as reducing the limit to 80 GW·s and keeping these generators online, since the nuclear plants contribute a large portion of the grid's system inertia. Emissions also increased by ~25% in the modeled scenarios where these nuclear plants were retired. If the critical inertia limit was kept the same (100 GW·s), adding 525 MW of fast frequency response from wind, solar, and energy storage could reduce the number of critical inertia hours by 86% with a response time of 15 cycles. Therefore, while the transition to a grid with mostly non-synchronous energy generation should be handled with care, many feasible pathways for integrating inverter-connected technologies and maintaining a stable grid exist. Building on the prior two methods, a third method was developed to evaluate the impact of energy storage systems on grid stability and system cost. While many grid-scale energy storage projects have been built and several have been announced, energy storage is costly and could negatively impact grid stability if systems are connected non-synchronously. Three different energy storage technologies with varying durations, ramp rates, and costs were modeled using a linearized dispatch model with discrete transmission zones and sub-hourly intervals (i.e. 15 minutes). Small penetrations of these technologies were modeled in a grid dominated by non-synchronous generation (51% wind and solar) to identify optimal storage zones. Transmission zones in the North, Northwest, West, Far West, and Panhandle regions were found to be the most favorable for building grid-scale storage from an economic standpoint. Next, higher energy storage penetrations were modeled to analyze the impact of storage on system inertia and the system cost. These high penetration scenarios focused primarily on storage divided across the optimal storage zones in proportion to their system cost impact. The modeling results showed that flywheels were able to maintain higher system inertia levels. Even so, the system cost was much lower when compressed air energy storage systems were modeled, demonstrating that high-duration energy storage technologies provided the most value to the grid. Energy storage was also more effective at maintaining grid stability and reducing costs than peaking plants. As a result, our model showed that new peakers might not be revenue sufficient in zones with high penetrations of renewable energy and energy storage. Many options exist for reliably integrating high penetrations of variable renewable energy generation, including an inertia market, synthetic and virtual inertia, and grid-scale storage, but few of these solutions are available today. Together, each of the analyses presented in this dissertation communicate when grid stability issues might occur and how low system inertia levels could be avoided
This study presents options to speed up the deployment of wind power, both onshore and offshore, until 2050. It builds on IRENA’s global roadmap to scale up renewables and meet climate goals.
This dissertation, "Optimal Planning and Management of Stochastic Demand and Renewable Energy in Smart Power Grid" by Kwok-kei, Simon, Ng, 吳國基, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: To combat global climate change, the reduction of carbon emissions in different industries, particularly the power industry, has been gradually moving towards a low-carbon profile to alleviate any irreversible damage to the planet and our future generations. Traditional fossil-fuel-based generation is slowly replaced by more renewable energy generation while it can be harnessed. However, renewables such as solar and wind are stochastic in nature and difficult to predict accurately. With the increasing content of renewables, there is also an increasing challenge to the planning and operation of the grid. With the rapid deployment of smart meters and advanced metering infrastructure (AMI), an emerging approach is to schedule controllable end-use devices to improve energy efficiency. Real-time pricing signals combined with this approach can potentially deliver more economic and environmental advantages compared with the existing common flat tariffs. Motivated by this, the thesis presents an automatic and optimal load scheduling framework to help balance intermittent renewables via the demand side. A bi-level consumer-utility optimization model is proposed to take marginal price signals and wind power into account. The impact of wind uncertainty is formulated in three different ways, namely deterministic value, scenario analysis, and cumulative distributions function, to provide a comprehensive modeling of unpredictable wind energy. To solve the problem in off-the-shelf optimization software, the proposed non-linear bi-level model is converted into an equivalent single-level mixed integer linear programming problem using the Karush-Kuhn-Tucker optimality conditions and linearization techniques. Numerical examples show that the proposed model is able to achieve the dual goals of minimizing the consumer payment as well as improving system conditions. The ultimate goal of this work is to provide a tool for utilities to consider the demand response model into their market-clearing procedure. As high penetration of distributed renewable energy resources are most likely applied to remote or stand-alone systems, planning such systems with uncertainties in both generation and demand sides is needed. As such, a three-level probabilistic sizing methodology is developed to obtain a practical sizing result for a stand-alone photovoltaic (PV) system. The first-level consists of three modules: 1) load demand, 2) renewable resources, and 3) system components, which comprise the fundamental elements of sizing the system. The second-level consists of various models, such as a Markov chain solar radiation model and a stochastic load simulator. The third-level combines reliability indices with an annualized cost of system to form a new objective function, which can simultaneously consider both system cost and reliability based on a chronological Monte Carlo simulation and particle swamp optimization approach. The simulation results are then tested and verified in a smart grid laboratory at the University of Hong Kong to demonstrate the feasibility of the proposed model. In summary, this thesis has developed a comprehensive framework of demand response on variable end-use consumptions with stochastic generation from renewables while optimizing both reliability and cost. Smart grid technologies, such as renewables, microgrid, storage, load signature, and demand
Wind and solar generation have gained a significant momentum in the last five years in the United States. According to the American Wind Energy Association, the installed wind power capacity has tripled from 25,410 MW in early 2009 to 74,472 MW as of the end of 2015. Meanwhile, solar photovoltaic (PV) is reported that its capacity has skyrocketed from 298 MW in 2009 to 7,260 MW in 2015 by the Solar Energy Industries Association. Despite the fact that wind and solar only make up 4.4% and 0.4%, respectively, of total electricity generation in 2014, the nation is right on its track to the Department of Energy (DOE)’s goal of 20% wind and 14% solar by year 2030. The future of renewable energy is aspiring. The rapid growth in renewable generation results in an urge to studying the reliability implication of renewable integration. For this purpose, two DOE projects were funded to the University of Tennessee, Knoxville, and the Oak Ridge National Laboratory. The first project, Grid Operational Issues and Analyses of the Eastern Interconnection (EI), is aimed at studying the dynamic stability impact of high wind penetration on the U.S. EI system in year 2030. The second project, Frequency Response Assessment and Improvement of Three Major North American Interconnections due to High Penetrations of Photovoltaic Generation, concentrates on the influence of high solar penetration on primary frequency response. This thesis documents the efforts of the above-mentioned two projects. Chapter 1 gives an introduction on power system dynamic modeling. Chapter 2 describes the process of dynamic models development. Chapter 3 discusses the adoption of synchro-phasor measurement for system-level dynamic model validation and the impact of turbine governor deadband on system dynamic response. Chapter 4 presents a stability impact study of high wind penetration on the U.S. Eastern Grid. Chapter 5 documents the modeling and simulation of the EI system under high solar penetration. Chapter 6 summaries two dynamic model reduction studies on the EI system. Conclusions, a summary of the major contribution of the Ph.D. work, and a discussion of possible future work are given in Chapter 7.
In the United States and elsewhere, renewable energy (RE) generation supplies an increasingly large percentage of annual demand, including nine U.S. states where wind comprised over 10% of in-state generation in 2013. This white paper summarizes the challenges to integrating increasing amounts of variable RE, identifies emerging practices in power system planning and operation that can facilitate ability to respond to change in demand and supply, as they must accommodate variable and uncertain load. Power system operators have thus been able to accommodate increased variable RE largely without substantial new investment in system flexibility, such as new storage, demand response, or generation dedicated to addressing RE variability and uncertainty. To achieve higher penetration levels, multiple grid integration studies in the United States have evaluated scenarios where an economic carrying capacity of at least 30% is achieved via transmission expansion and largely understood changes to system operations. Studies have also demonstrated that carrying capacity is not fixed and can be improved through technical and institutional changes. This creates the possibility to achieve even higher penetration levels through strategic investments in both demand- and supply-side sources of flexibility.