Dissertations / Theses on the topic 'Unit commitment and economic dispatch'

Optimal generation scheduling attempts to minimise the cost of power production while satisfying the various operation constraints and physical limitations on the power system components. The thermal generation scheduling problem can be considered as a power system control problem acting over different time frames. The unit commitment phase determines the optimum pattern for starting up and shutting down the generating units over the designated scheduling period, while the economic dispatch phase is concerned with allocation of the load demand among the on-line generators. In a hydrothermal system the optimal scheduling of generation involves the allocation of generation among the hydro electric and thermal plants so as to minimise total operation costs of thermal plants while satisfying the various constraints on the hydraulic and power system network. This thesis reports on the development of genetic algorithm computation techniques for the solution of the short term generation scheduling problem for power systems having both thermal and hydro units. A comprehensive genetic algorithm modelling framework for thermal and hydrothermal scheduling problems using two genetic algorithm models, a canonical genetic algorithm and a deterministic crowding genetic algorithm, is presented. The thermal scheduling modelling framework incorporates unit minimum up and down times, demand and reserve constraints, cooling time dependent start up costs, unit ramp rates, and multiple unit operating states, while constraints such as multiple cascade hydraulic networks, river transport delays and variable head hydro plants, are accounted for in the hydraulic system modelling. These basic genetic algorithm models have been enhanced, using quasi problem decomposition, and hybridisation techniques, resulting in efficient generation scheduling algorithms. The results of the performance of the algorithms on small, medium and large scale power system problems is presented and compared with other conventional scheduling techniques.

The solution of generation scheduling (GS) problems involves the determination of the unit commitment (UC) and economic dispatch (ED) for each generator in a power system at each time interval in the scheduling period. The solution procedure requires the simultaneous consideration of these two decisions. In recent years researchers have focused much attention on new solution techniques to GS. This paper proposes the application of a variety of genetic algorithm (GA) based approaches and investigates how these techniques may be improved in order to more quickly obtain the optimum or near optimum solution for the GS problem. The results obtained show that the GA-based hybrid approach offers an effective alternative for solving realistic GS problems within a realistic timeframe.

This thesis aims to provide a general overview of a cost and benefit analysis of incorporating a battery energy storage system within unit commitment model. The deregulation of the electricity market in the U.S. has only been around for the last two decades. With renewable energy and energy storage systems becoming less expensive, a decentralized market scheme is becoming more popular and plausible. The scope of this work is to provide a fundamental understanding of unit commitment and a cost analysis of applying a battery energy storage system to an already established power system. A battery energy storage system (BESS) was placed within a unit commitment schematic and modeled for a 7 day/168 hour forecast. Three models were generated, two with and one without the battery energy storage device (BESS). The comparison between the three systems was conducted to produce a visual economic justification to the feasibility of a BESS.

Today the modern power system is more dynamic and its operation is a subject to a number of constraints that are reflected in various management and planning tools used by system operators. In the case of hourly generation planning, Economic Dispatch (ED) allocates the outputs of all committed generating units, which are previously identified by the solution of the Unit Commitment (UC) problem. Thus, the accurate solutions of the ED and UC problems are essential in order to operate the power system in an economic and efficient manner. A number of computation techniques have progressively been proposed to solve these critical issues. One of them is a Particle Swarm Optimisation (PSO), which belongs to the evolutionary computation techniques, and it has attracted a great attention of the research community since it has been found to be extremely effective in solving a wide range of engineering problems. The attractive characteristics of PSO include: ease of implementation, fast convergence compared with the traditional evolutionary computation techniques and stable convergence characteristic. Although the PSO algorithms can converge very quickly towards the optimal solutions for many optimisation problems, it has been observed that in problems with a large number of suboptimal areas (i.e. multi-modal problems), PSO could get trapped in those local minima, including ED and UC problems. Aiming at enhancing the diversity of the traditional PSO algorithms, this thesis proposes a method of combining the PSO algorithms with a real-valued natural mutation (RVM) operator to enhance the global search capability and investigate the performance of the proposed algorithm compared with the standard PSO algorithms and other algorithms. Prior to applying to ED and UC problems, the proposed method is tested with some selected mathematical functions where the results show that it can avoid being trapped in local minima. The proposed methodology is then applied to ED and UC problems, and the obtained results show that it can provide solutions with good accuracy and stable convergence characteristic with simple implementation and satisfactory calculation time. Furthermore, the sensitivity analysis of PSO parameters has been studied so as to investigate the response of the proposed method to the parameter variations, especially in both ED and UC problems. The outcome of this research shows that the proposed method succeeds in dealing with the PSO' s drawbacks and also shows the superiority over the traditional PSO algorithms and other methods in terms of high quality solutions, stable convergence characteristic, and robustness.

In modern days electric power systems, the penetration of renewable resources and the introduction of free market principles have led to new challenges facing the power producers and regulators. Renewable production is intermittent which leads to fluctuations in the grid and requires more control for regulators, and the free market principle raises the challenge for power plant producers to operate their plants in the most profitable way given the fluctuating prices. Those problems are addressed in the literature as the Economic Dispatch, and they have been discussed from both regulator and producer view points. Combined Cycle Power plants have the privileges of being dispatchable very fast and with low cost which put them as a primary solution to power disturbance in grid, this fast dispatch-ability also allows them to exploit price changes very efficiently to maximize their profit, and this sheds the light on the importance of prices forecasting as an input for the profit optimization of power plants. In this project, an integrated solution is introduced to optimize the dispatch of combined cycle power plants that are bidding for electricity markets, the solution is composed of two models, the forecasting model and the optimization model. The forecasting model is flexible enough to forecast electricity and fuel prices for different markets and with different forecasting horizons. Machine learning algorithms were used to build and validate the model, and data from different countries were used to test the model. The optimization model incorporates the forecasting model outputs as inputs parameters, and uses other parameters and constraints from the operating conditions of the power plant as well as the market in which the plant is selling. The power plant in this mode is assumed to satisfy different demands, each of these demands have corresponding electricity price and cost of energy not served. The model decides which units to be dispatched at each time stamp to give out the maximum profit given all these constraints, it also decides whether to satisfy all the demands or not producing part of each of them.

The integration of variable renewable energy sources (VRE) is likely to cause fundamental and structural changes to the operation of future power systems. In the United Kingdom (UK), large amounts of price-insensitive and variable-output wind generation is expected to be deployed to contribute towards renewable energy and carbon dioxide (CO2) emission targets. Wind generation, with near-zero marginal costs, limited predictability, and a limited ability to provide upward dispatch, displaces price-setting thermal power plants, with higher marginal costs, changing flexibility and reserve requirements. New-build, commercial-scale, and low-carbon generation capacity, such as CO2 capture and storage (CCS) and nuclear, may impact power system flexibility and ramping capabilities. Low-carbon generation portfolios with price-sensitive thermal power plants and energy storage are therefore likely to be required to manage increased levels of variability and uncertainty at operational timescales. This work builds on a high-resolution wind reanalysis dataset of UK wind sites. The locations of existing and proposed wind farms are used to produce plausible and internally consistent wind deployment scenarios that represent the spatial distribution of future UK wind capacity. Temporally consistent electricity demand data is used to characterise and assess demand-wind variability and net demand ramp events. A unit commitment and economic dispatch (UCED) model is developed to evaluate the likely operating regimes of thermal power plants and CCS-equipped units across a range of future UK wind scenarios. Security constraints for reserve and power plant operating constraints, such as power output limits, ramp rates, minimum up/down times, and start-up times, ensure the operational feasibility of dispatch schedules. The load factors, time spent at different loads, and the ramping and start-up requirements of thermal power plants are assessed. CO2 duration curves are developed to assess the impacts of increasing wind capacity on the distribution of CO2 emissions. A sensitivity analysis investigates the impacts of part-load efficiency losses, ramp rates, minimum up/down times, and start-up/shut-down costs on power plant operating regimes and flexibility requirements. The interactions between a portfolio of energy storage units and flexible CO2 capture units are then explored. This multi-disciplinary research presents a temporally-explicit and detailed assessment of operational flexibility requirements at full 8760 hour resolution, highlighting the non-linear impacts of increasing wind capacity. The methodological framework presented here uses high spatial-and temporal-resolution wind data but is expected to provide useful insights for other VREbased power systems to mitigate the implications of inadequate flexibility.

This dissertation focuses on selected issues in regard to the mathematical modeling of electricity markets. In a first step the interrelations of electric power market modeling are highlighted a crossroad between operations research, applied economics, and engineering. In a second step the development of a large-scale continental European economic engineering model named ELMOD is described and the model is applied to the issue of wind integration. It is concluded that enabling the integration of low-carbon technologies appears feasible for wind energy. In a third step algorithmic work is carried out regarding a game theoretic model. Two approaches in order to solve a discretely-constrained mathematical program with equilibrium constraints using disjunctive constraints are presented. The first one reformulates the problem as a mixed-integer linear program and the second one applies the Benders decomposition technique. Selected numerical results are reported.

Fundação de Apoio a Pesquisa e à Inovação Tecnológica do Estado de Sergipe - FAPITEC/SE
The growth of the electric energy consumption in the last years has generated the need of the increase in the amount of power sources, making the electricity sector undergo some large changes. This has provided the search for tools that promotes a better efficiency and security to the electrical power systems. A planning problem that is considered important in the daily operation of the power systems is the Unit Commitment, where the time schedule of the operation is defined, determining which machines will be online or offline, and which are the operating points. Those units must operate by load variation, respecting the operative and security constraints. This research proposes the resolution of the problem for the short-term planning, taking a set of constraints associated with the thermal generation and the power system. Among them, we can highlight the output power variation constraints of the machines and the security restrictions of the transmission system, avoided in most Unit Commitment studies. This problem is nonlinear, mixed-integer and has a large scale. The methodology used involves the utilization of an Adaptive Genetic Algorithm, for the Unit Commitment problem, and the Interior-Point Primal- Dual Predictor–Corrector Method, for DC power flow resolution in economic dispatch problem. Furthemore, this research proposes the implementation of cross-over and mutation operators of Genetic Algorithm based on a ring methodology applied in Unit Commitment matrix. The results were obtained through simulations in a mathematical simulation software, using the IEEE test systems with 30 bus and 9 generators, and another with 24 bus and 26 generators. The validation of the algorithm was done by comparing the results with other works in the literature.
O crescimento do consumo de energia elétrica nos últimos anos vem gerando a necessidade de um aumento na quantidade de fontes geradoras, fazendo com que o setor elétrico passe por grandes mudanças. Isso tem proporcionado a busca por ferramentas que ofereçam maior eficiência e segurança aos sistemas de potência. Um problema considerado de extrema importância na operação diária dos sistemas elétricos é o planejamento da Alocação das Unidades Geradoras, onde define-se a programação horária das unidades do sistema, determinando quais máquinas deverão estar ligadas ou desligadas, e quais serão seus respectivos pontos de operação. Essas unidades geradoras devem operar de forma eficaz, mediante a variação da carga, respeitando restrições operativas e de segurança do sistema. Este trabalho propõe a resolução do problema para o planejamento de curto prazo, levando em consideração uma série de restrições relacionadas a geração térmica e ao sistema elétrico. Entre elas, podemos destacar as restrições de variação de potência de saída das máquinas e as restrições de segurança do sistema de transmissão, evitadas na maioria dos estudos de Alocação de Unidades Geradoras. Este problema tem característica não-linear, inteiro-misto e de grande escala. A metodologia utilizada para resolução do problema envolve a utilização de um Algoritmo Genético Adaptativo, para Alocação das Unidades, e o Método de Pontos Interiores Primal-Dual Preditor-Corretor, para a resolução do Fluxo de Potência Ótimo DC no problema do Despacho Econômico. Além disso, este trabalho propõe a implementação dos operadores de cross-over e mutação do Algoritmo Genético com base em uma metodologia anelar aplicada na matriz de alocação de unidades. Os resultados foram obtidos através de simulações em um software de simulação matemática, utilizando os sistemas testes do IEEE de 30 barras com 9 geradores e 24 barras com 26 geradores, e a validação do algoritmo foi feita comparando os resultados obtidos com os outros trabalhos da literatura.

In this thesis, I present four research papers related by their focus on environmental and economic effects of low-carbon policies and technologies in electric power. The papers address a number of issues related to the operation and design of CCS-equipped plants with solvent storage and bypass, the effect of Renewable Portfolio Standards (RPS) on cycling of coal-fired power plants, and the EPA’s proposed CO2 emissions rule for existing power plants. In Chapter 2, I present results from a study of the design and operation of power plants equipped with CCS with flue gas bypass and solvent storage. I considered whether flue gas bypass and solvent storage could be used to increase the profitability of plants with CCS. Using a pricetaker profit maximization model, I evaluated the increase in NPV at a pulverized coal (PC) plant with an amine-based capture system, a PC plant with an ammonia-based capture system, and a natural gas combined-cycle plant with an amine-based capture system when these plants were equipped with an optimally sized solvent storage vessel and regenerator. I found that while flue gas bypass and solvent storage increased profitability at low CO2 prices, they ceased to do so at CO2 prices high enough for the overall plant to become NPV-positive. In Chapter 3, I present results from a Unit Commitment and Economic Dispatch model of the PJM West power system. I quantify the increase in cycling of coal-fired power plants that results when complying with a 20% RPS using wind power, accounting for cycling costs not usually included in power plant bids. I find that while additional cycling does increase cycling-related production costs and emissions of CO2, SO2, and NOX, these increases are small compared to the overall reductions in production costs and air emissions that occur with high levels of wind. In proposing its existing power plant CO2 emissions standard, the Environmental Protection Agency determined that significant energy efficiency would be available to aid in compliance. In Chapter 4, I use an expanded version of the model of Chapter 3 to evaluate compliance with the standard with and without this energy efficiency, as well as under several other scenarios. I find that emissions of CO2, SO2, and NOX are relatively insensitive to the amount of energy efficiency available, but that production costs increase significantly when complying without efficiency. In complying with the EPA’s proposed existing power plant CO2 emissions standard, states will have the choice of whether to comply individually or in cooperation with other states, as well as the choice of whether to comply with a rate-based standard or a mass-based standard. In Chapter 5, I present results from a linear dispatch model of the power system in the continental U.S. I find that cooperative compliance reduces total costs, but that certain states will prefer not to cooperate. I also find that compliance with a mass-based standard increases electricity prices by a larger margin than does compliance with a rate-based standard, with implications for the distribution of surplus changes between producers and consumers.

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.

Submitted by Renata Lopes (renatasil82@gmail.com) on 2016-12-21T10:50:23Z No. of bitstreams: 1 flaviarodriguesdonascimento.pdf: 1211614 bytes, checksum: ab9ba99ac0572dc9242451b399b808c5 (MD5)
Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2016-12-22T12:00:31Z (GMT) No. of bitstreams: 1 flaviarodriguesdonascimento.pdf: 1211614 bytes, checksum: ab9ba99ac0572dc9242451b399b808c5 (MD5)
Made available in DSpace on 2016-12-22T12:00:31Z (GMT). No. of bitstreams: 1 flaviarodriguesdonascimento.pdf: 1211614 bytes, checksum: ab9ba99ac0572dc9242451b399b808c5 (MD5) Previous issue date: 2011-09-15
CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
A programação diária da operação de sistemas termoelétricos de geração consiste em determinar uma estratégia de despacho das unidades geradoras para atender a demanda de energia, satisfazendo as restrições operacionais e funcionais do sistema elétrico de potência. O problema pode ser dividido em dois subproblemas: (i) referente à determinação das unidades que devem estar em operação mediante a demanda solicitada, “Thermal Unit Commitment” e (ii) referente à determinação da potência gerada por cada uma das unidades colocadas em serviço, “Despacho Econômico”. Devido à variação de carga ao longo do tempo, a programação da operação envolve decisões do sistema de geração a cada hora, dentro do horizonte de um dia a duas semanas. Os estudos relacionados às técnicas de otimização bio-inspiradas, utilizadas na resolução da programação diária da operação de sistemas termoelétricos de geração, apontam que a combinação entre os métodos computacionais biologicamente inspirados com outras técnicas de otimização tem papel importante na obtenção de melhores soluções em um menor tempo de processamento. Seguindo esta linha de pesquisa, o presente trabalho faz uso de uma metodologia baseada na otimização por colônia de formiga para a minimização do custo da programação diária de operação de unidades termoelétricas. O modelo proposto utiliza uma Matriz de Sensibilidade (MS) baseada nas informações fornecidas pelos multiplicadores de Lagrange a fim de melhorar o processo de busca bio-inspirado. Desta forma, um percentual dos indivíduos da colônia faz uso destas informações no processo evolutivo da colônia. Os resultados alcançados através das simulações indicam que a utilização da MS resulta em soluções de qualidade com um número reduzido de indivíduos.
The daily schedule of thermoelectric systems consists of determining the strategy to set the generation units to be put in operation to meet the load, meeting also the operational and functional constraints of the respective power system. This problem can be split into two subproblems: (i) schedule of units that must operate in accordance with a given load, or Thermal Unit Commitment and (ii) set the power generation for each committed unit, or Economical Schedule. Due to load variations the schedule involves hourly generation decisions, in a horizon that varies from one day to two weeks. Researches related to bio-inspired optimization strategies applied to the daily thermal system operation show that the combination between bio-inspired computing techniques and other optimization methods has an important role in order to obtain better solutions in a shorter computing time. Following this, the present work makes use of a methodology based on Ant Colony Optimization to minimize the costs of the thermal system daily scheduling. This proposed method uses a Sensitivity Matrix (SM) based on information from Lagrange Multipliers related to the problem in order to improve the bio-inspired process. In this way, a percentage of the individuals make use of the provided information in the colony evolution process. The results obtained through those simulations indicate that the use of the SM presents better quality solutions with a reduced number of individuals.

No
A genetic algorithm (GA) augmented with knowledge-based methods has been developed for solving the unit commitment economic dispatch problem. The GA evolves a population of binary strings which represent commitment schedules. The initial population of schedules is chosen using a method based on elicited scheduling knowledge. A fast rule-based dispatch method is then used to evaluate candidate solutions. The knowledge-based genetic algorithm is applied to a test system of ten thermal units over 24-hour time intervals, including minimum on/off times and ramp rates, and achieves lower cost solutions than Lagrangian relaxation in comparable computational time.

碩士
國立中山大學
電機工程學系研究所
104
The advances of renewable energy in power system not only reduced more environmental pollution than using traditional method, but provided alternative programs. As increasing of those unstable supply of green power. It will impact on the system. Such as system reliability, cost of power, power quality, power stability, etc. Therefore, how to stabilize the system while the load keep changing with ancillary service is an important issue currently. This thesis studies two case, 1th, combined thermal power generator, wind power, solar power, battery storage system to form a system, and reach the goal of security dispatch and the function of demand response by battery storage system. Second, analysis ancillary service of power system day-ahead market without battery storage system, including automatic generation control, spinning reserve, and supplemental reserve. Using improved Bee Swarm Optimization (BSO) to solve unit commitment and economic dispatch problem. This thesis proposed the adaptive inertia weight rule into BSO, and improve the mathematics formula to avoid the local optimality problem and scout bee consider global optimality only, which can quickly reach the optimal solution with a better performance and accuracy.

abstract: Traditional deterministic reserve requirements rely on ad-hoc, rule of thumb methods to determine adequate reserve in order to ensure a reliable unit commitment. Since congestion and uncertainties exist in the system, both the quantity and the location of reserves are essential to ensure system reliability and market efficiency. The modeling of operating reserves in the existing deterministic reserve requirements acquire the operating reserves on a zonal basis and do not fully capture the impact of congestion. The purpose of a reserve zone is to ensure that operating reserves are spread across the network. Operating reserves are shared inside each reserve zone, but intra-zonal congestion may block the deliverability of operating reserves within a zone. Thus, improving reserve policies such as reserve zones may improve the location and deliverability of reserve. As more non-dispatchable renewable resources are integrated into the grid, it will become increasingly difficult to predict the transfer capabilities and the network congestion. At the same time, renewable resources require operators to acquire more operating reserves. With existing deterministic reserve requirements unable to ensure optimal reserve locations, the importance of reserve location and reserve deliverability will increase. While stochastic programming can be used to determine reserve by explicitly modelling uncertainties, there are still scalability as well as pricing issues. Therefore, new methods to improve existing deterministic reserve requirements are desired. One key barrier of improving existing deterministic reserve requirements is its potential market impacts. A metric, quality of service, is proposed in this thesis to evaluate the price signal and market impacts of proposed hourly reserve zones. Three main goals of this thesis are: 1) to develop a theoretical and mathematical model to better locate reserve while maintaining the deterministic unit commitment and economic dispatch structure, especially with the consideration of renewables, 2) to develop a market settlement scheme of proposed dynamic reserve policies such that the market efficiency is improved, 3) to evaluate the market impacts and price signal of the proposed dynamic reserve policies.
Dissertation/Thesis
Doctoral Dissertation Electrical Engineering 2015

Emerging integration of renewable energy has prompted a wide range of research on the use of energy storage to compensate for the added uncertainty that accompanies these resources. In the Electric Reliability Council of Texas (ERCOT), compressed air energy storage (CAES) has drawn particular attention because Texas has suitable geology and also lacks appropriate resources and locations for pumped hydroelectric storage (PHS). While there have been studies on incorporation of renewable energy, utilization of energy storage, and dispatch optimization, this is the first body of work to integrate all these subjects along with the proven ability to recreate historical dispatch and price conditions. To quantify the operational behavior, economic feasibility, and environmental impacts of CAES, this work utilized sophisticated unit commitment and dispatch (UC&D) models that determine the least-cost dispatch for meeting a set of grid and generator constraints. This work first addressed the ability of these models to recreate historical dispatch and price conditions through a calibration analysis that incorporated major model improvements such as capacity availability and sophisticated treatment of combined heat and power (CHP) plants. These additions appreciably improved the consistency of the model results when compared to historical ERCOT conditions. An initial UC&D model was used to investigate the impacts on the dispatch of a future high wind generation scenario with the potential to utilize numerous CAES facilities. For all future natural gas prices considered, the addition of CAES led to reduced use of high marginal cost generator types, increased use of base-load generator types, and average reductions in the total operating costs of 3.7 million dollars per week. Additional analyses demonstrated the importance of allowing CAES to participate in all available energy and ancillary services (AS) markets and that a reduction in future thermal capacity would increase the use of CAES. A second UC&D model, which incorporated advanced features like variable marginal heat rates, was used to analyze the influence of future wind generation variability on the dispatch and resulting environmental impacts. This analysis revealed that higher amounts of wind variability led to an increase in the daily net load ramping requirements which resulted in less use of coal and nuclear generators in favor of faster ramping units along with reductions in emissions and water use. The changes to the net load also resulted in increased volatility of the energy and AS prices between daily minimum and maximum levels. These impacts were also found to increase with compounding intensity as higher levels of wind variability were reached. Lastly, the advanced UC&D model was also used to evaluate the operational behavior and potential economic feasibility of a first entrant conventional or adiabatic CAES system. Both storage systems were found to operate in a single mode that enabled very high utilization of their capacity indicating both systems have highly desirable characteristics. The results suggest that there is a positive case for the investment in a first entrant CAES facility in the ERCOT market.
text

The objective of this PhD thesis is to create a Unit Commitment and Economic Dispatch (UCED) modelling tool that can used to simulate the deterministic performance of a power system with thermal and renewable generators and energy storage technologies. The model was formulated using mixed integer programing (MIP) on GAMS interface. A robust commercial solver by IBM (CPLEX) is used as solver. Emphasis on the development of the tool has been given on the following aspects. a) Technical impacts of Variable Renewable Energy (VRE) integration. The UCED model developed in this thesis is a high resolution short-term dispatch model. It captures the variability of VRE power on the intra-hour level. In addition the model considers a large number of important real world, system, unit and policy constraints. Detailed representation of a power system allows for a realistic estimation of maximum penetration levels of VRE and the related technical impacts like cycling of generators (part-loading and number of start-ups). b) CO2 emissions. High levels of VRE penetration can potentially increase consumption of fuel in thermal units per unit of electricity produced due to increased thermal cycling. The dispatch of units in the UCED model is based on minimizing system wide operational costs the most important of those being fuel, start-up costs and the cost of carbon. Fuel consumption is calculated using technical data from Input/Output curves of individual generators. The start-up cost is calculated based on times the generator units have been off and the energy requirement to bring the unit back to hot state. Thus dynamic changes on fuel consumption can be captured and reported. c) Technical solutions to facilitate VRE integration. VRE penetration can be facilitated if appropriate solutions are implemented. Energy storage is an effective way to reduce the impact of RE variability. The UCED model includes an integrated Mixed Integer Linear (MILP) compressed air energy storage (CAES) simulation sub-model. Unlike existing CAES models, the new “Thermo-Economic” (TE) CAES model developed in this thesis uses technical data from major CAES manufacturers to model the dynamic effect of cavern pressure on both the compression and expansion sides during CAES operation. More specifically the TE model takes into account that a) a compressor discharges at a pressure equal to the back-pressure developed in the cavern at each moment, b) the speed of charging can be regulated through inlet guide vanes; higher charging speed can take place at the expense of additional power consumption, c) the maximum power output during expansion can be limited by the levels of cavern pressure; there is a threshold pressure level below which the maximum output decreases linearly with pressure. Since it uses actual power curves to simulate CAES operation, the TE model can be assumed to be more accurate than conventional Fixed Parameter (FP) models that don’t model dynamic effects of cavern pressure on CAES operation. The TE model in this thesis is compared with conventional FP models using historical market prices from the Irish electricity market. The comparison was based on the ability of a CAES unit to arbitrage energy for making profit in the Irish electricity market. More specifically a “Base” scenario was created that included the operation of a 270MW CAES unit with technical characteristics obtained from a major CAES manufacturer and assumed discharge time of 13hr. Various sensitivities on discharge time, natural gas prices and system marginal prices (SMPs) were modeled. An additional scenario was created to show the benefit on CAES profitability if the unit participated in both the energy and ancillary services markets. All scenarios were modeled using both the TE and FP CAES models. The results showed that the most realistic TE model returns around 15% less profitability across more scenarios. The reduction in profitability grows to around 30% when the cavern volume (discharge time) is reduced to half (6 hours). The latter is related to the sensitivity of the TE model on cavern pressure that is being built faster when the volume is reduced. A CAES unit won’t get a positive net present value (NPV) in Ireland under any scenario unless SMPs are greatly increased. Thus, it was shown that that existing FP CAES models overestimate CAES profitability. More accurate models need to be used to estimate CAES profitability in deregulated markets. Additionally, it might deem necessary to create additional markets for energy storage units and increase the possible revenue sources and magnitude to facilitate an increase of storage capacity worldwide. The second step of analysis involved the integration of the CAES and UCED models. The UCED model developed in this thesis was validated and applied using data from the Irish grid, a power system with more than 50 thermal generators. A vast of existent data was used to create a mathematical model of the Irish system. Such data include technical specifications and variables of thermal generators, maintenance schedules and historical solar, wind and demand data. The validation exercise was deemed successful since the UCED model simulated utilization factors of 45 out of 52 generators with an absolute difference between modeled and actual results on utilization factors of less than 6% (the absolute differences are called Delta in this thesis). In addition the results of validation exercise were compared with the results of a similar exercise where PLEXOS was the modelling tool and it was found that the results of the two models were similar for the vast majority of generators. More specifically, the PLEXOS model results showed higher deltas for the coal-fired generators compared to the UCED model. On the other hand the UCED model, reported higher delta values for peat-fired generators. The results of the PLEXOS model were slightly better for the gas-fired generators while both models reported deltas nearly zero for all oil and distillate-fired generators. Finally the model was applied to study the benefits of energy storage in Ireland in 2020 when wind penetration is expected to reach 37% of total demand. The analysis involved the development of two groups of 3 scenarios each. In the first group the main scenario also called the “Reference” was used to simulate the short-term unit (30 min step) commitment within the Irish system without storage. The results of the reference scenario were compared with two additional scenarios that assumed the existence of one 270MW CAES unit in Northern Ireland by 2020 (again the first scenario involved the TE and the second the FP CAES model). The results showed –when using the TE model- that the inclusion of one 270MW CAES unit in AI can help reduce wind curtailment by 88GWh, CO2 emissions by 150,000 tonnes and system costs by € 6 million per year. If an FP model had been used instead the reductions would be: wind curtailment by 108GWh, CO2 emissions by 270,000 tonnes and annual system costs by €13 million. Two main conclusions can be obtained from the specific set of results. The first conclusion is that storage units have a financial benefit over the whole system. Thus, when a CAES unit operates to minimize the costs of the whole system can incur substantially more benefits compared to if the CAES unit operated to maximize the individual unit’s profits as in the case presented earlier. The benefits of storage over the whole system should be accounted to make policy decisions and create incentives for investors to increase energy storage capacity in national grids. The second important conclusion is that existing CAES FP models overestimate the ability of a CAES unit to facilitate VRE penetration. More accurate TE models should be used to assess a unit’s capability to increase system flexibility. A second group of scenarios was created to simulate the benefit of CAES at even higher VRE penetration levels. In the second group the “Reference” scenario again, assumed no storage however, wind production was increased by 25%. Again the “Reference” was compared with two additional scenarios that assumed integration of 3x270MW=810MW of storage capacity in AI (one scenario used the TE model and the other the FP). The results for the TE model show that each of the 3 CAES units reduces wind curtailment by 188,000MWh, total system costs by €29 million and CO2 emissions by 180,000 tonnes. The same reductions for the FP model are 217,000MWh of wind curtailment, €25.6 million on total system costs and 180,000 tonnes of CO2. Thus, the results of the second group of scenarios show that as the installed capacity of both CAES and wind increases in Ireland a) the system-wide benefits of CAES increase and b) the differences on results between the TE and FP models become much smaller.

The future smart grid is envisioned as a large scale cyber-physical system encompassing advanced power, computing, communications and control technologies. This work provides comprehensive accounts of the application with optimization methods, probability theory, commitment and dispatching technologies for addressing open problems in three emerging areas that pertain to the smart grid: unit commitment, service restoration problems in microgrid systems, and charging services for the plug-in hybrid electric vehicle (PHEV) markets. The work on the short-term scheduling problem in renewable-powered islanded microgrids is to determine the least-cost unit commitment (UC) and the associated dispatch, while meeting electricity load, environmental and system operating requirements. A novel probability-based concept, {\em probability of self-sufficiency}, is introduced to indicate the probability that the microgrid is capable of meeting local demand in a self-sufficient manner. Furthermore, we make the first attempt in approaching the mixed-integer UC problem from a convex optimization perspective, which leads to an analytical closed-form characterization of the optimal commitment and dispatch solutions. The extended research of the renewable-powered microgrid in the connection mode is the second part of this work. In this situation, the role of microgrid is changed to be either an electricity provider selling energy to the main grid or a consumer purchasing energy from the main grid. This interaction with the main grid completes work on the scheduling schemes. Third, a microgrid should be connected with the main grid most of the time. However, when a blackout of the main grid occurs, how to guarantee reliability in a microgrid as much as possible becomes an immediate question, which motivates us to investigate the service restoration in a microgrid, driven islanded by an unscheduled breakdown from the main grid. The objective is to determine the maximum of the expected restorative loads by choosing the best arrangement of the power network configurations immediately from the beginning of the breakdown all the way to the end of the island mode. Lastly, the work investigating the pricing strategy in future PHEV markets considers a monopoly market with two typical service classes. The unique characteristics of battery charging result in a piecewise linear quality of service model. Resorting to the concept of subdifferential, some theoretical results, including the existence and uniqueness of the subscriber equilibrium as well as the convergence of the corresponding subscriber dynamics are established. In the course of developing revenue-maximizing pricing strategies for both service classes, a general tradeoff has been identi ed between monetization and customer acquisition.
Graduate

Combined heat and power (CHP) is a technology that decreases total fuel consumption and related greenhouse gas emissions by producing both electricity and useful thermal energy from a single energy source. In the industrial and commercial sectors, a typical CHP site relies upon the electricity distribution network for significant periods, i.e., for purchasing power from the grid during periods of high demand or when off-peak electricity tariffs are available. On the other hand, in some cases, a CHP plant is allowed to sell surplus power to the grid during on-peak hours when electricity prices are highest while all operating constraints and local demands are satisfied. Therefore, if the plant is connected with the external grid and allowed to participate in open energy markets in the future, it could yield significant economic benefits by selling/buying power depending on market conditions. This is achieved by solving the power system generation scheduling problem using mathematical programming. In this work, we present the application of mixed-integer nonlinear programming (MINLP) approach for scheduling of a CHP plant in the day-ahead wholesale energy markets. This work employs first principles models to describe the nonlinear dynamics of a CHP plant and its individual components (gas and steam turbines, heat recovery steam generators, and auxiliary boilers). The MINLP framework includes practical constraints such as minimum/maximum power output and steam flow restrictions, minimum up/down times, start-up and shut-down procedures, and fuel limits. We provide case studies involving the Hal C. Weaver power plant complex at the University of Texas at Austin to demonstrate this methodology. The results show that the optimized operating strategies can yield substantial net incomes from electricity sales and purchases. This work also highlights the application of a nonlinear model predictive control scheme to a heavy-duty gas turbine power plant for frequency and temperature control. This scheme is compared to a classical PID/logic based control scheme and is found to provide superior output responses with smaller settling times and less oscillatory behavior in response to disturbances in electric loads.
text

Σκοπός αυτής της Διπλωματικής είναι η μελέτη του προβλήματος οικονομικής κατανομής φορτίου και η μελέτη του προβλήματος ένταξης μονάδων ενός καθαρά θερμικού συστήματος. Το πρόβλημα της οικονομικής κατανομής φορτίου και της ένταξης μονάδων εξετάζονται αρχικά θεωρητικά. Στα πλαίσια αυτής της Διπλωματικής γίνεται και εφαρμογή της οικονομικής κατανομής φορτίου και της ένταξης μονάδων σ' ένα ενδεικτικό δίκτυο δοκιμών με χρήση Η/Υ. Για την οικονομική κατανομή φορτίου χρησιμοποιείται το πρόγραμμα Economic Dispatch Program και για την ένταξη μονάδων, τα προγράμματα Unit Commitment και Unitcom. Επίσης εξετάζεται συνοπτικά το Ελληνικό σύστημα ηλεκτρικής ενέργειας και δίνονται τα βασικά σημεία της Απελευθέρωσης Αγοράς Ηλεκτρικής Ενέργειας στην Ελλάδα.
This diploma essay's purpose is to study the economic dispatch problem and the unit commitment problem of a simple thermal power system. Initially, the economic dispatch problem and the unit commitment problem are examined theoretically. In this essay takes place an application of the economic dispatch problem and an application of the unit commitment problem, in an indicative test network, with the use of a PC. For the economic dispatch problem is used the Economic Dispatch Program and for the unit commitment problem are used the Unit Commitment and Unitcom programs. Furthermore, in this essay are concisely examined the Hellenic power system and the release of the Hellenic electric market.

This dissertation focuses on selected issues in regard to the mathematical modeling of electricity markets. In a first step the interrelations of electric power market modeling are highlighted a crossroad between operations research, applied economics, and engineering. In a second step the development of a large-scale continental European economic engineering model named ELMOD is described and the model is applied to the issue of wind integration. It is concluded that enabling the integration of low-carbon technologies appears feasible for wind energy. In a third step algorithmic work is carried out regarding a game theoretic model. Two approaches in order to solve a discretely-constrained mathematical program with equilibrium constraints using disjunctive constraints are presented. The first one reformulates the problem as a mixed-integer linear program and the second one applies the Benders decomposition technique. Selected numerical results are reported.

In the US, seven regional transmission organizations (RTOs) operate wholesale electricity markets within three largely independent transmission systems, the largest of which includes five RTO regions and many vertically integrated utilities.

RTOs operate a day-ahead and a real-time market. In the day-ahead market, generation and demand-side resources are optimally scheduled based on bids and offers for the next day. Those schedules are adjusted according to actual operating conditions in the real-time market. Both markets involve a unit commitment calculation, a mixed integer program that determines which generators will be online, and an economic dispatch calculation, an optimization determines the output of each online generator for every interval and calculates locational marginal prices (LMPs).

The use of LMPs for the management of congestion in RTO transmission systems has brought efficiency and transparency to the operation of electric power systems and provides price signals that highlight the need for investment in transmission and generation. Through this work, we aim to extend these efficiency and transparency gains to the coordination across RTOs. Existing market-based inter-regional coordination schemes are limited to incremental changes in real-time markets.

We propose a multi-regional unit-commitment that enables coordination in the day-ahead timeframe by applying a distributed approach to approximate a system-wide optimal commitment and dispatch while allowing each region to largely maintain their own rules, model only internal transmission up to the boundary, and keep sensitive financial information confidential. A heuristic algorithm based on an extension of the alternating directions method of multipliers (ADMM) for the mixed integer program is applied to the unit commitment.

The proposed coordinated solution was simulated and compared to the ideal single-market scenario and to a representation of the current uncoordinated solution, achieving at least 58% of the maximum potential savings, which, in terms of the annual cost of electric generation in the US, could add up to nearly $7 billion per year. In addition to the coordinated day-ahead solution, we develop a distributed solution for financial transmission rights (FTR) auctions with minimal information sharing across RTOs that constitutes the first known work to provide a viable option for market participants to seamlessly hedge price variability exposure on cross-border transactions.

Carbon dioxide (CO₂) capture and sequestration (CCS) at fossil-fueled power plants is a critical technology for CO₂ emissions mitigation during the transition to a sustainable energy system. Post-combustion amine scrubbing is a relatively mature CO₂ capture technology, but barriers to implementation include high capital costs and energy requirements that reduce net power output by 20-30%. Capture energy requirements are typically assumed constant, but work investigates whether flexibly operating amine scrubbing systems in response to electricity market conditions can add value to CO₂ capture facilities while maintaining environmental benefits. Two versatile optimization models have been created to study the electricity system implications of flexible CO₂ capture. One model assesses the value of flexible capture at a single facility in response to volatile electricity prices, while the other represents a full electricity system to study the ability of flexible capture to meet electricity demand and reliability (ancillary) service requirements. Price-responsive flexible CO₂ capture has limited value at market conditions that justify CO₂ capture investments. Solvent storage can add value for price arbitrage by allowing flexible operation without additional CO₂ emissions, but only with favorable capital costs. The primary advantage of flexible CO₂ capture is an increased ability to provide grid reliability services and improve grid resiliency at minimum and maximum electricity demand. Flexibility mitigates capacity shortages because capture energy requirements need not be replaced, and variable capture at low demand helps respond to intermittent renewable generation.
text

In the effort to reduce the release of harmful gases associated with the transportation sector, Plug-in Electric Vehicles (PEV) have been deployed on the account of zero-tail pipe emissions. With electrification of transport it is imperative to address the electrical grid emissions during vehicle charging by motivating the use of distributed generation. This thesis employs optimal charging strategies based on solar availability and electrical grid tariffs to minimize the cost of retrofitting an existing parking lot with photovoltaic (PV) and PEV infrastructure. The optimization is cast as a unit-commitment problem using the CPLEX optimization tool to determine the optimal charge scheduling. The model determines the optimal capacity of system components and assesses the techno-economic feasibility of PV infrastructure in the microgrid by minimizing the net present cost (NPC) in two case studies: Victoria, BC and Los Angeles, CA. It was determined that due to a relatively low grid tariff and scarcity of solar irradiation, it is not economically feasible to install solar panels and coordination of charging reduces the operating cost by 11% in Victoria. Alternatively, with a high grid tariff and abundance of solar radiation, it shown that Los Angeles is a promising candidate for PV installations. With the implementation of a charging coordination scheme in this region, NPC savings of 8-16% are simulated with the current prices of solar infrastructure. Additionally, coordinated charging was assessed in conjunction with various commercial buildings posing as a base load and it was determined that the effects of coordination were more prominent with smaller base loads.
Graduate

The growing interest in small-scale electricity generation located near customers, known as Distributed Generation (DG), is driven primarily by emerging technologies, environmental regulations and concerns, electricity market restructuring, and growing customer demand for increased quality and reliability of the electricity supply. Wind turbines are one of the renewable DG technologies that have become an important source of electricity in many parts of the world. Wind power can be used in many places to provide a viable solution to rising demand, energy security and independence, and climate change mitigation. This research aims broadly at facilitating the integration of wind-based DG without jeopardizing the system’s economics and reliability. To achieve this goal, the thesis tackles wind power from three perspectives: those of the policy maker, the investor, and the system operator. Generally, the economic viability of a project is determined within the framework of relevant policies. Therefore, these policies influence the decisions of potential investors in wind power. From this perspective, chapters 3 and 4 investigate the influence of policies on the economic viability of wind-based DG projects. In chapter 3, the role of Ontario’s taxation and incentive policies in the economic viability of wind-based DG projects is investigated. In this study, the effects of provincial income taxes, capital cost allowances, property taxes, and relevant federal incentives are considered. Net Present Value (NPV) and Internal Rate of Return (IRR) for different scenarios are used to assess the project’s viability under the Ontario Standard Offer Program (SOP) for wind power. In chapter 4, the thesis proposes the use of wind power as a source of electricity in a new city being developed in the Duqm area of Oman, where no policies supporting renewable energy exist. The study shows that the cost of electricity produced by wind turbines is higher than that of the existing generation system, due to the subsidized prices of domestically available natural gas. However, given high international natural gas prices, the country’s long-term Liquefied Natural Gas (LNG) export obligations, and the expansion of natural gas-based industries, investments in wind power in Duqm can be justified. A feed-in tariff and capital cost allowance policies are recommended to facilitate investments in this sector. From a wind-based DG investor’s perspective, the optimal selection of wind turbines can make wind power more economical, as illustrated in chapters 5 and 6. In chapter 5, the thesis presents a new generic model for Capacity Factor (CF) estimation using wind speed characteristics at any site and the power performance curve parameters of any pitch-regulated wind turbine. Compared to the existing model, the proposed formulation is simpler and results in more accurate CF estimation. CF models can be used by wind-based DG investors for optimal turbine-site matching applications. However, in chapter 6, the thesis demonstrates that using CF models as the sole basis for turbine-site matching applications tends to produce results that are biased towards higher towers but do not include the associated costs. Therefore, a novel formulation for the turbine-site matching problem, based on a modified CF formulation that does include turbine tower height, is introduced in chapter 6. The proposed universal Turbine-Site Matching Index (TSMI) also includes the effects of turbine rated power and tower height on the initial capital cost of wind turbines. Chapter 7 tackles wind power from a power system operator’s perspective. Despite wind power benefits, the effects of its intermittent nature on power systems need to be carefully examined as penetration levels increase. In this chapter, the thesis investigates the effects of different temporal wind profiles on the scheduling costs of thermal generation units. Two profiles are considered: synoptic-dominated and diurnal-dominated variations of aggregated wind power. To simulate wind profile impacts, a linear mixed-integer unit commitment problem is formulated in a GAMS environment. The uncertainty associated with wind power is represented using a chance constrained formulation. The simulation results illustrate the significant impacts of different wind profiles on fuel saving benefits, startup costs, and wind power curtailments. In addition, the results demonstrate the importance of the wide geographical dispersion of wind power production facilities to minimize the impacts of network constraints on the value of the harvested wind energy and the amount of curtailed energy.