This PhD thesis proposes a bottom-up approach that accurately addresses the operational flexibility embedded in each part of a multi-energy system (MES). Several models which cover the simulations from replicating domestic electrified demands to power system scheduling are proposed. More specifically, a domes-tic multi-energy consumption model is firstly developed to simulate one minute resolution energy profiles of individual dwellings with the installation of prospec-tive technologies (i.e., electric heat pumps (EHPs), electric vehicles (EVs)). After-wards, a fast linear programming (LP) unit commitment (UC) model is devel-oped with the consideration of characteristics of generators and a full set of ancil-lary services (i.e., frequency response and reserves). More importantly, the fre-quency response requirements in low inertia systems are assessed with the con-sideration of three grid frequency regulations (i.e., rate of change of frequency, Nadir and quasi-steady state). Furthermore, the UC model has integrated vari-ous flexibility contributors in MES to provide ancillary and flexibility services, which include pumped hydro storages (PHSs), interconnectors, batteries and demand side resources (i.e., individual EHPs, heat networks, electrolysers). More importantly, the fast frequency response (FFR) provision from nonsynchronous resources is implemented and the demand response application of electrolysers is taken as an example to provide FFR in the UC model. By using the integrated UC model with the consideration of flexibility services provided by resources in the MES, the advantages of multi-energy operation can be clearly identified which can be used to inform system operators and policy makers to design and operate energy systems in a more economic and environment-friendly way.

The reliability issue of the infrastructure systems has become one of the major concerns of the system operators. This dissertation is a collection of four published and working papers that address the specific reliable design and operations problems from three different application settings: transportation/telecommunications network, distribution network, and power plant. In these four projects, key random factors like site disruption and uncertain demand are explicitly considered and proper research tools including stochastic programming, robust optimization, and variants of robust optimization are applied to formulate the problems based on which the important and challenging modelling elements (nonlinear congestion, disruption caused demand variation, etc.) can be introduced and studied. Besides, for each of the optimization models, we also develop advanced solution algorithms that can solve large-scale instances within a short amount of time and devise comprehensive numerical experiments to derive insights. The modelling techniques and solution methods can be easily extended to study reliability issues in other applications.

Recently, the concept of Microgrids (MG) has been introduced in the distribution network. Microgrids are defined as small power systems that consist of various distributed micro generators that are capable of supplying a significant portion of the local demand. Microgrids can operate in grid-connected mode, in which they are connected to the upstream grid, or in isolated mode, where they are disconnected from the upstream grid and the local generators are the only source of power supply. In order to maximize the benefits of the resources available in a microgrid, an optimal scheduling of the power generation is required. Renewable resources have an intermittent nature that causes uncertainties in the system. These added uncertainties must be taken into consideration when solving the generation scheduling problem in order to obtain reliable solutions. This research studies the scheduling of power generation in a microgrid that has a group of dispatchable and non-dispatchable generators. The operation of a microgrid during grid-connected mode and isolated mode is analyzed under variable demand profiles. Two mixed integer linear programming (MILP) models for the day-ahead unit commitment problem in a microgrid are proposed. Each model corresponds to one mode of operation. Uncertainty handling techniques are integrated in both models. The models are solved using the General Algebraic Modeling System (GAMS). A number of study cases are examined to study the operation of the microgrid and to evaluate the effects of uncertainties and spinning reserve requirement on the microgrid’s expenses.

A spinning reserve assessment technique for a deregulated system has been developed and presented in this thesis. The technique is based on direct search optimization approach. Computer programs have been developed to implement the optimization processes both for transmission loss and without transmission loss.<p> A system commits adequate generation to satisfy its load and export/import commitment. Additional generation known as spinning reserve is also required to satisfy unforeseen load changes or withstand sudden generation loss. In a vertically integrated system, a single entity generates, transmits and distributes electrical energy. As a part of its operational planning, the single entity decides the level of spinning reserve. The cost associated with generation, transmission, distribution including the spinning reserve is then passed on to the customers.<p> In a deregulated system, generation, transmission and distribution are three businesses. Generators compete with each other to sell their energy to the Independent System Operators (ISO). ISO coordinates the bids from the generation as well as the bids from the bulk customers. In order to ensure a reliable operation, ISO must also ensure that the system has adequate spinning reserve. ISO must buy spinning reserve from the spinning reserve market. A probabilistic method called the load forecast uncertainty (LFU)-based spinning reserve assessment (LSRA) is proposed to assess the spinning reserve requirements in a deregulated power system.<p> The LSRA is an energy cost- based approach that incorporates the load forecast uncertainty of the day-ahead market (DAM) and the energy prices within the system in the assessment process. The LSRA technique analyzes every load step of the 49-step LFU model and the probability that the hourly DAM load will be within that load step on the actual day. Economic and reliability decisions are made based on the analysis to determine and minimize the total energy cost for each hour subject to certain system constraints in order to assess the spinning reserve requirements. The direct search optimization approach is easily implemented in the determination of the optimal SR requirements since the objective function is a combination of linear and non-linear functions. This approach involves varying the amount of SR within the system from zero to the maximum available capacity. By varying the amount of SR within the system, the optimal SR for which the hourly total operating cost is minimum and all operating constraints are satisfied is evaluated.<p> One major advantage of the LSRA technique is the inclusion of all the major system variables like DAM hourly loads and energy prices and the utilization of the stochastic nature of the system components in its computation. The setback in this technique is the need to have access to historical load data and spot market energy prices during all seasons. The availability and reliability of these historical data has a huge effect on the LSRA technique to adequately assess the spinning reserve requirements in a deregulated system.<p> The technique, along with the effects of load forecast uncertainty, energy prices of spinning reserve and spot market and the reloading up and down limits of the generating zones on the spinning reserve requirements are illustrated in detail in this thesis work. The effects of the above stochastic components of the power system on the spinning reserve requirements are illustrated numerically by different graphs using a computer simulation of the technique incorporating test systems with and without transmission loss.

Wind power is variable, uncertain, intermittent and site specific. The operating capacity credit associated with a wind farm is therefore considerably different from that assigned to a conventional generating unit and as wind penetrations in conventional power systems increase, it is vital that wind power be fully integrated in power system planning and operating protocols.<p> The research described in this thesis is focused on the determination of the operating capacity benefits associated with adding wind power to a conventional power system. Probabilistic techniques are used to quantify the risk and operating capacity benefits under various risk criteria. A short term wind speed probability distribution and short term wind power probability distribution forecasting model is presented and a multi-state model of a wind farm is utilized to determine several operating performance indices. The concepts and developed model are illustrated by application to two published test systems. The increase in peak load carrying capability attributable to added wind power is examined under a range of system operating conditions that include the effects of seasonality, locality and wind parameter trends. The operating capacity credit associated with dependent and independent wind farms is also examined. The dependent and independent conditions provide boundary values that clearly indicate the effects of wind speed correlation. Well-being analyses which incorporate the accepted deterministic criterion in an evaluation of the system operating state probabilities is applied to the wind integrated test systems using a novel approach to calculate the operating state probabilities. Most modern power systems are interconnected to one or more other power systems and therefore have increased access and exposure to wind power. This thesis examines the risk benefits associated with wind integrated interconnected power systems under various conditions using the two test systems.<p> The research described in this thesis clearly illustrates that the operating capacity benefits associated with wind power can be quantified and used in making generating capacity scheduling decisions in a wind integrated power system.

A spinning reserve assessment technique for a deregulated system has been developed and presented in this thesis. The technique is based on direct search optimization approach. Computer programs have been developed to implement the optimization processes both for transmission loss and without transmission loss.<p> A system commits adequate generation to satisfy its load and export/import commitment. Additional generation known as spinning reserve is also required to satisfy unforeseen load changes or withstand sudden generation loss. In a vertically integrated system, a single entity generates, transmits and distributes electrical energy. As a part of its operational planning, the single entity decides the level of spinning reserve. The cost associated with generation, transmission, distribution including the spinning reserve is then passed on to the customers.<p> In a deregulated system, generation, transmission and distribution are three businesses. Generators compete with each other to sell their energy to the Independent System Operators (ISO). ISO coordinates the bids from the generation as well as the bids from the bulk customers. In order to ensure a reliable operation, ISO must also ensure that the system has adequate spinning reserve. ISO must buy spinning reserve from the spinning reserve market. A probabilistic method called the load forecast uncertainty (LFU)-based spinning reserve assessment (LSRA) is proposed to assess the spinning reserve requirements in a deregulated power system.<p> The LSRA is an energy cost- based approach that incorporates the load forecast uncertainty of the day-ahead market (DAM) and the energy prices within the system in the assessment process. The LSRA technique analyzes every load step of the 49-step LFU model and the probability that the hourly DAM load will be within that load step on the actual day. Economic and reliability decisions are made based on the analysis to determine and minimize the total energy cost for each hour subject to certain system constraints in order to assess the spinning reserve requirements. The direct search optimization approach is easily implemented in the determination of the optimal SR requirements since the objective function is a combination of linear and non-linear functions. This approach involves varying the amount of SR within the system from zero to the maximum available capacity. By varying the amount of SR within the system, the optimal SR for which the hourly total operating cost is minimum and all operating constraints are satisfied is evaluated.<p> One major advantage of the LSRA technique is the inclusion of all the major system variables like DAM hourly loads and energy prices and the utilization of the stochastic nature of the system components in its computation. The setback in this technique is the need to have access to historical load data and spot market energy prices during all seasons. The availability and reliability of these historical data has a huge effect on the LSRA technique to adequately assess the spinning reserve requirements in a deregulated system.<p> The technique, along with the effects of load forecast uncertainty, energy prices of spinning reserve and spot market and the reloading up and down limits of the generating zones on the spinning reserve requirements are illustrated in detail in this thesis work. The effects of the above stochastic components of the power system on the spinning reserve requirements are illustrated numerically by different graphs using a computer simulation of the technique incorporating test systems with and without transmission loss.

Wind power is variable, uncertain, intermittent and site specific. The operating capacity credit associated with a wind farm is therefore considerably different from that assigned to a conventional generating unit and as wind penetrations in conventional power systems increase, it is vital that wind power be fully integrated in power system planning and operating protocols.<p> The research described in this thesis is focused on the determination of the operating capacity benefits associated with adding wind power to a conventional power system. Probabilistic techniques are used to quantify the risk and operating capacity benefits under various risk criteria. A short term wind speed probability distribution and short term wind power probability distribution forecasting model is presented and a multi-state model of a wind farm is utilized to determine several operating performance indices. The concepts and developed model are illustrated by application to two published test systems. The increase in peak load carrying capability attributable to added wind power is examined under a range of system operating conditions that include the effects of seasonality, locality and wind parameter trends. The operating capacity credit associated with dependent and independent wind farms is also examined. The dependent and independent conditions provide boundary values that clearly indicate the effects of wind speed correlation. Well-being analyses which incorporate the accepted deterministic criterion in an evaluation of the system operating state probabilities is applied to the wind integrated test systems using a novel approach to calculate the operating state probabilities. Most modern power systems are interconnected to one or more other power systems and therefore have increased access and exposure to wind power. This thesis examines the risk benefits associated with wind integrated interconnected power systems under various conditions using the two test systems.<p> The research described in this thesis clearly illustrates that the operating capacity benefits associated with wind power can be quantified and used in making generating capacity scheduling decisions in a wind integrated power system.

The objective of thermal unit commitment is to schedule the on or off status and the real power outputs of units and minimize the system production cost during the period while simultaneously satisfying operational constraints. In this thesis, the Real Genetic Algorithms (RGA) and the Hybrid Taguchi-Genetic Algorithm (HTGA) approaches are presented to solve the thermal unit commitment problem, and comparison with the results obtained using GA. Then this thesis applied the systematic reasoning ability of the Taguchi method operated after mutation can promote the RGA efficiency. The objective of Taguchi method is to improve the quality of offsprings by optimizing themselves to generate a better result, because the offsprings produced randomly by crossover and mutation process is not necessary better than the parents. This method can not only enhance the neighborhood search, but can also search the optimum solution quickly to advance convergence. Finally, it will be shown that the HTGA outperforms RGA by comparing simulation results of unit commitment.

To meet ambitious upcoming state renewable portfolio standards (RPSs), respond to customer demand for “green” electricity choices and to move towards more renewable, domestic and clean sources of energy, many utilities and power producers are accelerating deployment of wind, solar photovoltaic and solar thermal generating facilities. These sources of electricity, particularly wind power, are highly variable and difficult to forecast. To manage this variability, utilities can increase availability of fossil fuel-dependent backup generation, but this approach will eliminate some of the emissions benefits associated with renewable energy. Alternately, energy storage could provide needed ancillary services for renewables. Energy storage could also support other operational needs for utilities, providing greater system resiliency, zero emission ancillary services for other generators, faster responses than current backup generation and lower marginal costs than some fossil fueled alternatives. These benefits might justify the high capital cost associated with energy storage. Quantitative analysis of the role energy storage can have in improving economic dispatch, however, is limited. To examine the potential benefits of energy storage availability, a generalized unit commitment model of thermal generating units and energy storage facilities is developed. Initial study will focus on the city of Austin, Texas. While Austin Energy’s proximity to and collaborative partnerships with The University of Texas at Austin facilitated collaboration, their ambitious goal to produce 30-35% of their power from renewable sources by 2020, as well as their continued leadership in smart grid technology implementation makes them an excellent initial test case. The model developed here will be sufficiently flexible that it can be used to study other utilities or coherent regions. Results from the energy storage deployment scenarios studied here show that if all costs are ignored, large quantities of seasonal storage are preferred, enabling storage of plentiful wind generation during winter months to be dispatched during high cost peak periods in the summer. Such an arrangement can yield as much as $94 million in yearly operational cost savings, but might cost hundreds of billions to implement. Conversely, yearly cost reductions of $40 million can be achieved with one CAES facility and a small fleet of electrochemical storage devices. These results indicate that small quantities of storage could have significant operational benefit, as they manage only the highest cost hours of the year, avoiding the most expensive generators while improving utilization of renewable generation throughout the year. Further study using a modified unit commitment model can help to narrow the performance requirements of storage, clarify optimal storage portfolios and determine the optimal siting of this storage within the grid. / text

In optimization problems involving uncertainty, probabilistic constraints are an important tool for defining safety of decisions. In Energy management, many optimization problems have some underlying uncertainty. In particular this is the case of unit commitment problems. In this Thesis, we will investigate probabilistic constraints from a theoretical, algorithmic and applicative point of view. We provide new insights on differentiability of probabilistic constraints and on convexity results of feasible sets. New variants of bundle methods, both of proximal and level type, specially tailored for convex optimization under probabilistic constraints, are given and convergence shown. Both methods explicitly deal with evaluation errors in both the gradient and value of the probabilistic constraint. We also look at two applications from energy management: cascaded reservoir management with uncertainty on inflows and unit commitment with uncertainty on customer load. In both applications uncertainty is dealt with through the use of probabilistic constraints. The presented numerical results seem to indicate the feasibility of solving an optimization problem with a joint probabilistic constraint on a system having up to 200 constraints. This is roughly the order of magnitude needed in the applications. The differentiability results involve probabilistic constraints on uncertain linear and nonlinear inequality systems. In the latter case a convexity structure in the underlying uncertainty vector is required. The uncertainty vector is assumed to have a multivariate Gaussian or Student law. The provided gradient formulae allow for efficient numerical sampling schemes. For probabilistic constraints that can be rewritten through the use of Copulae, we provide new insights on convexity of the feasible set. These results require a generalized concavity structure of the Copulae, the marginal distribution functions of the underlying random vector and of the underlying inequality system. These generalized concavity properties may hold only on specific sets.