The objective of this research is to present a comprehensive framework for harnessing the flexibility of power systems in the presence of unforeseen events, such as those associated with component outages or renewable energy variability. Increased penetration of variable resources in the power grid, mainly in the form of wind and solar plants, has resulted in variable power flow patterns, increased thermal unit cycling and higher reserve capacity requirements. Furthermore, the variability of renewable energy output has increased the system’s ramping requirements and threatens the system’s voltage control capabilities. However, new sources of flexibility and network control are emerging to address these problems. Specifically, energy storage systems, demand side management, distributed energy resources and flexible transmission operation can participate by providing ramping services and/or voltage control, as well as by alleviating transmission congestion. This research focuses on contributing to modeling and optimization approaches for scheduling the operation of these sources of flexibility in a certain look-ahead horizon, ensuring a state of the art level of modeling accuracy, with full inclusion of voltage control considerations which do not exist in current DC-OPF modeling approaches. Also, by including reactive power flows, the network congestion model proposed is above par compared to the current state-of-the-art for look-ahead dispatch literature. Nevertheless, the model is further expanded by including a thermal model for transmission lines, which allows for the implementation of dynamic line ratings in look-ahead economic dispatch. The benefits from these augmented modeling capabilities are documented and compared with current operating practices. Once an AC-OPF look-ahead optimization problem has been established, and the corresponding components have been modeled, further contributions are made in the area of remedial action schemes. The developed formulations allow for the identification of appropriate corrective actions that will restore feasibility in infeasible cases. Finally, a combination of contingency filtering and contingency analysis approaches is developed, to allow for fast identification and analysis of critical outages in the transmission system. The filtering approach is based on a basic Taylor expansion of network power flow equations as well as a new formulation of margin indices that directly quantify the proximity to constraint violation in the post-outage system state. The analysis approach is based on low-rank modifications of the Jacobian matrix of network equations, to produce good estimates of post-outage operating states and map the effect on the system’s operating constraints. Compared to current state of the art, advances are made both in the speed and the accuracy of the analysis, since the proposed filtering and analysis methods are fully unbalanced. The need for unbalanced security analysis is discussed and justified. Through the contributions made in this research, a roadmap to increase flexibility in power system operations is developed. Namely, an enhanced modeling capability allows for integration of additional sources of flexibility and voltage control and a highly accurate security analysis and remedial actions formulation allows for improved response to unforeseen critical outages and rapid generation changes.

Electric power is a basic requirement for present day life and its various economic sectors. To satisfy the ever-increasing needs for electricity, the number of generating units, transmission lines and distribution systems is rising steadily. In addition, electric power systems are among the most complex industrial systems of the modern age. Beside complexity, the generation of electric power is a main source of gaseous emissions and pollutants. The planning and operation of electric power systems must be done in a way that the load demand is met reliably, cost-effectively and in an environmentally responsible manner. Practitioners strive to achieve these goals for successful planning and operations utilizing various optimization tools. It is clear that the objectives to be satisfied are mostly conflicting. In particular, minimizing the fuel cost and the gaseous emissions are two conflicting and non-commensurate objectives. Therefore, multi-objective optimization techniques are employed to obtain trade-off relationships between these incompatible objective functions in order to help decision makers take proper decisions. In this thesis, two main power system operation problems are addressed. These are the economic load dispatch (ED) and the short-term hydro-thermal generation scheduling (STHTS). They are treated first as single-objective optimization problems then they are tackled as multi-objective ones considering the environmental aspects. These problems, single and multi-objective, are nonlinear non-convex constrained optimization problems with high-dimensional search spaces. This makes them a real challenge for any optimization technique. To obtain the optimal or close to optimal solutions, a modified bacterial foraging algorithm is proposed, developed and successfully applied. The bacterial foraging algorithm is a metaheuristic non-calculus-based optimization technique. The proposed algorithm is validated using diverse benchmark optimization examples before implementing it to solve the problems of this thesis. Various practical constraints are considered in the different cases of each problem. These include transmission losses, valve-point effects for both the ED and the STHTS problems and water availability and reservoir configurations for the STHTS problem. In all cases the optimal or near-optimal solution is obtained. For the multi-objective optimization cases, the Pareto optimal solution set that shows the trade-off relationship between the conflicting objectives is successfully captured.

This research proposes an efficient reliability modeling and simulation methodology in power systems to include photovoltaic units, wind farms and storage. Energy losses by wake effect in a wind farm are incorporated. Using the wake model, wind shade, shear effect and wind direction are also reflected. For solar modules with titled surface, more accurate hourly photovoltaic power in a specific location is calculated with the physical specifications. There exists a certain level of correlation between renewable energy and load. This work uses clustering algorithms to consider those correlated variables. Different approaches are presented and applied to the composite power system, and compared with different scenarios using reliability analysis and simulation. To verify the results, reliability indices are compared with those from original data. As the penetration of renewables increases, the reliability issues will become more important because of the intermittent and non-dispatchable nature of these sources of power. Storage can provide the ability to regulate these fluctuations. The use of storage is investigated in this research. To determine the operating states and transition times of all turbines, Monte Carlo is used for system simulation in the thesis. A conventional power system from IEEE Reliability Test Systems is used with transmission line capacity, and wind and solar data are from National Climatic Data Center and National Renewal Energy Laboratory. The results show that the proposed technique is effective and efficient in practical applications for reliability analysis.

Motivated by the role of decarbonizing the electric power sector to mitigate climate change, I assess the economic and environmental merits of three key technologies for decarbonizing the electric power sector across four chapters in this thesis. These chapters explore how adding flexibility to power plants equipped with carbon capture and sequestration (CCS) affects system costs and carbon dioxide (CO2) emissions, how grid-scale electricity storage affects system CO2 emissions as a power system decarbonizes, and how distributed solar photovoltaic (distributed PV) electricity generation suppresses wholesale electricity prices. In each chapter, I address these questions through a combination of power system optimization, statistics, and techno-economic analysis, and tie my findings to policy implications. In Chapter 2, I compare the cost-effectiveness of “flexible” CCS retrofits to other compliance strategies with the U.S. Clean Power Plan (CPP) and a hypothetical stronger CPP. Relative to “normal” CCS, “flexible” CCS retrofits include solvent storage that allows the generator to temporarily eliminate the CCS parasitic load and increase the generator’s net efficiency, capacity, and ramp rate. Using a unit commitment and economic dispatch (UCED) model, I find that flexible CCS achieves more cost-effective emissions reductions than normal CCS under the CPP and stronger CPP, but that flexible CCS is less cost-effective than other compliance strategies under both reduction targets. In Chapter 3, I conduct a detailed comparison of how flexible versus normal CCS retrofits affect total system costs and CO2 emissions under a moderate and strong CO2 emission limit. Given that a key benefit of flexible CCS relative to normal CCS is increased reserve provision, I break total system costs into generation, reserve, and CCS capital costs. Using a UCED model, I find that flexible CCS retrofits reduce total system costs relative to normal CCS retrofits under both emission limits. Furthermore, 40-80% of these cost reductions come from reserve cost reductions. Accounting for costs and CO2 emissions, though, flexible CCS poses a trade-off to policymakers under the moderate emission limit, as flexible CCS increases system CO2 emissions relative to normal CCS. No such trade-off exists under the stronger emission limit, as flexible CCS reduces system CO2 emissions and costs relative to normal CCS. In Chapter 4, I quantify how storage affects operational CO2 emissions as a power system decarbonizes under a moderate and strong CO2 emission limit through 2045. In so doing, I aim to better understand how storage transitions from increasing CO2 emissions in historic U.S. systems to enabling deeply decarbonized systems. Additionally, under each target I compare how storage affects CO2 emissions when participating in only energy, only reserve, and energy and reserve markets. Using a capacity expansion (CE) model to forecast fleet changes through 2045 and a UCED model to quantify how storage affects system CO2 emissions, I find that storage quickly transitions from increasing to decreasing CO2 emissions under the moderate and strong emission limits. Whether storage provides only energy, only reserves, or energy and reserves drives large differences in the magnitude, but not the direction, of the effect of storage on CO2 emissions. In Chapter 5, I quantify a benefit of distributed photovoltaic (PV) generation often overlooked by value of solar studies, namely the market price response. By displacing high-cost marginal generators, distributed PV generation reduces wholesale electricity prices, which in turn reduces utilities’ energy procurement costs. Using 2013 through 2015 data from California including a database of all distributed PV systems in the three California investor owned utilities, we estimate historic hourly distributed PV generation in California, then link that generation to reduced wholesale electricity prices via linear regression. From 2013 through 2015, we find that distributed PV suppressed historic median hourly LMPs by up to $2.7-3.1/MWh, yielding avoided costs of up to $650-730 million. These avoided costs are smaller than but on the order of other avoided costs commonly included in value of solar studies, so merit inclusion in future studies to properly value distributed PV.

In Kombination mit erneuerbaren Energien (EEG) werden Elektrofahrzeuge (EVs) als wichtiger Bestandteil einer Transformation hin zu nachhaltigen Energiesystemen angesehen. Obwohl EVs heute nur einen geringen Anteil an der Fahrzeugdurchdringung in Deutschland darstellen, ist es das Ziel der Bundesregierung, dass im Jahr 2030 sechs Millionen EVs auf deutschen Straßen fahren sollen. Die Realisierung dessen hätte aufgrund des daraus resultierenden zusätzlichen Strombedarfs erhebliche Auswirkungen auf das Stromsystem. Wie hoch diese sind, hängt maßgeblich von der Ladestrategie der Fahrzeuge ab und ist der Forschungsgegenstand dieser Arbeit. Die übergeordnete ökonomische Fragestellung lautet: Welche Auswirkungen haben unterschiedliche EV-Ladestrategien auf Strommengen und -preise in einem Stromsystem mit einem hohen Anteil an erneuerbaren Energien? Zur Beantwortung dessen wird zunächst der zeitabhängige Strombedarf von EVs bewertet. Im Anschluss, werden die EV-Ladestrategien unkontrolliertes Laden (UNC), kostengesteuertes Laden (DSM) und bidirektionales Laden (V2G) in einem europäischen Strommarktmodell umgesetzt und die Auswirkungen quantifiziert. Dadurch wurden folgende Erkenntnisse erlangt: EVs tragen zu einer besseren Integration der EEG bei, da alle drei Ladestrategien deren Abregelung reduzieren. Der zusätzliche Spitzenlastbedarf aufgrund von UNC wird je Millionen EVs im schlimmsten Fall auf 560 MW geschätzt. Entsprechend des Fahrverhaltens variiert die Stromnachfrage stark zwischen Werktagen und Wochenendtagen. An Werktagen sind die Spitzenwerte fast dreimal so hoch wie an Wochenendtagen. Wird durch UNC die Stromnachfrage erhöht, bedarf es des vermehrten Einsatzes von Spitzenlastkraftwerken, was zu steigenden Preisspitzen führt. Im Gegensatz dazu verschieben die beiden flexiblen Ladestrategien DSM und V2G die EV-Stromnachfrage in Zeiten mit geringer residualer Netzlast bzw. bei V2G deutlich zugunsten von Kraftwerken mit den niedrigsten Grenzkosten. Dies führt bei DSM zu einer Anhebung der Preise in Schwachlastzeiten. Bei V2G wird die Preisstruktur erheblich geglättet, indem Spitzenlastpreise reduziert und Schwachlastpreise deutlich erhöht werden. An Wochenenden ist dieser Effekt bei V2G noch stärker als an Werktagen, da ein großer Teil der EVs als stationärer Speicher genutzt werden kann. Neben ökonomischer Effizienz hat dies teilweise unerwünschte ökologische Nebenwirkungen. So werden im Fall von V2G bei niedrigen CO2-Preisen emissionsintensive Technologien wie Braunkohlekraftwerke begünstigt. Nichtsdestotrotz führen systemische Effekte, nämlich die Reduzierung von EEG-Abschaltungen, die Substitution von Spitzenlastkraftwerken und ein erhöhter Stromaustausch mit den Nachbarländern zu einer Gesamtreduktion der CO2-Emissionen. Bei hohen CO2-Preisen sind die Effekte durch V2G hinsichtlich der CO2-Emissionen und der ökonomischen Effizienz durchweg positiv. Begrenzt werden diese Vorteile von V2G durch wirtschaftliche Sättigungseffekte, welche bereits ab zwei Millionen Fahrzeugen deutlich werden. / In combination with renewable energy sources (RES), electric vehicles (EVs) are seen as an important element of a transformation towards sustainable energy systems. Although EVs currently represent only a small fraction of vehicle penetration in Germany, it is the goal of the German government to have six million EVs on German roads by 2030. The achievement of this would have a significant impact on the electricity system due to the resulting additional energy demand. How large these impacts are is the subject of this work. The overarching economic research question is: What effects do different EV charging strategies have on quantities and prices in a power system with a high share of RES? To answer this question, the time-dependent electricity demand of EVs is initially evaluated. Subsequently, the EV charging strategies uncontrolled charging (UNC), demand side management (DSM), in the sense of cost effective charging and bidirectional charging, i.e. vehicle-to-grid (V2G) are implemented in a European electricity market model and the impacts quantified. To summarize the findings: EVs contribute to the integration of RES, since all three charging strategies reduce curtailment. In the worst case scenario, the additional peak load demand due to UNC is estimated at 560 MW per million EVs. The demand for electricity varies greatly between working days and weekend days, depending on the driving patterns. On working days, the peak demand is almost three times as high as on weekend days. Overall, UNC leads to the increased use of peak load power plants, which leads to rising price peaks. In contrast, the two flexible charging strategies DSM and V2G shift the EVs' electricity demand in times of low residual grid load or, in the case of V2G, significantly in favour of the power plants with the lowest marginal costs. With DSM, this results in an increase in prices during off-peak periods. With V2G, the price structure is considerably smoothed by reducing peak load prices and significantly increasing off-peak prices. On weekend days this effect is even stronger with V2G than on working days, since a large part of the EVs can be used as stationary storage. In addition to economic effciency, this has partly undesirable ecological side effects. In the case of V2G, emission-intensive technologies such as lignite-fired power plants are promoted at low CO2 prices. Nevertheless, systemic effects, namely the reduction of RES curtailment, the substitution of peak load power plants, and an increased electricity exchange with neighboring countries, lead to an overall reduction of the CO2 emissions. These benefits of V2G are limited due to economic saturation effects, which are already noticeable starting at two million vehicles.

Electrical energy is the basic necessity for the economic development of human societies. In recent decades, the electricity industry is undergoing enormous changes, which have evolved into a large-scale and competitive industry. The integration of volatile renewable energy, and the emergence of transmission switching (TS) techniques bring great challenges to the existing power system operations problems, especially security-constrained unit commitment (SCUC) solution engines. In order to deal with the uncertainty of volatile renewable energy, scenario-based stochastic optimization approach has been widely employed to ensure the reliability and economic of power systems, in which each scenario would represent a possible system situation. Meanwhile, the emergence of TS techniques allows the system operators to change the topology of transmission systems in order to improve economic benefits by mitigating transmission congestion. However, with the introduction of extra scenarios and decision variables, the complexity of the SCUC model increases dramatically and more computational efforts are required, which might make the power system operation problems difficult to solve and even intractable. Therefore, an advanced solution technique is urgently needed to solve both stochastic SCUC problems and TS-based SCUC problems in an effective and fast way. In this dissertation, a decomposition framework is presented for the optimal operation of the large-scale power system, which decomposes the original large-size power system optimization problem into smaller-size and tractable subproblems, and solves these decomposed subproblems in a parallel manner with the help of high performance computing techniques. Numerical case studies on a modified I 118-bus system and a practical 1168-bus system demonstrate the effectiveness and efficiency of the proposed approach which will offer the power system a secure and economic operation under various uncertainties and contingencies.

In this research, a focus is on the development of simplified models to represent the behavior of electric machinery within the time-domain models of power systems. Toward this goal, a generator model is considered in which the states include the machine’s active and reactive power. In the case of the induction machine, rotor slip is utilized as a state and the steady-state equivalent circuit of the machine is used to calculate active and reactive power. The power network model is then configured to accept the generator and induction machine active and reactive power as inputs and provide machine terminal voltage amplitude and angle as outputs. The potential offered by these models is that the number of dynamic states is greatly reduced compared to traditional machine models. This can lead to increased simulation speed, which has potential benefits in model-based control. A potential disadvantage is that the relationship between the reactive power and terminal voltage requires the solution of nonlinear equations, which can lead to challenges when attempting to predict system dynamics in real-time optimal control. In addition, the accuracy of the generator model is greatly reduced with variations in rotor speed. Evaluation of the models is performed by comparing their predictions to those of traditional machine models in which stator dynamics are included and neglected.

As the electricity demand increases dramatically in Sweden, the need of using the existing electricity grid as efficiently as possible gains more importance. Simultaneously as needs expand, so does production in the form of wind parks and solar parks. This has led to an increase in connection requests at Svenska Kraftnät, the Swedish transmission system operator. The current process for accepting or rejecting these requests is based on the first-come-first-serve principle, where each request is investigated separately. This thesis investigates an alternative way of processing the requests in clusters and optimizing which combination is the best to accept from a technical point of view. To handle this multiobjective combinatorial optimization problem, a multiobjective Genetic algorithm with a Pareto filter is developed. The Genetic Algorithm finds a refined Pareto front containing optimal solutions that are plotted with objective function values. The user can then easily analyze the optimal solutions and decide upon which the final optimal request combination is. The developed Genetic Algorithm reaches a close-optimal Pareto front estimation after exploring between 15-40% of the solution space.

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