30 research outputs found

    Esclonamento de máquinas de cogeração utilzando programação inteira mista

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    As centrais termoelétricas convencionais convertem apenas parte do combustível consumido na produção de energia elétrica, sendo que outra parte resulta em perdas sob a forma de calor. Neste sentido, surgiram as unidades de cogeração, ou Combined Heat and Power (CHP), que permitem reaproveitar a energia dissipada sob a forma de energia térmica e disponibilizá-la, em conjunto com a energia elétrica gerada, para consumo doméstico ou industrial, tornando-as mais eficientes que as unidades convencionais Os custos de produção de energia elétrica e de calor das unidades CHP são representados por uma função não-linear e apresentam uma região de operação admissível que pode ser convexa ou não-convexa, dependendo das caraterísticas de cada unidade. Por estas razões, a modelação de unidades CHP no âmbito do escalonamento de geradores elétricos (na literatura inglesa Unit Commitment Problem (UCP)) tem especial relevância para as empresas que possuem, também, este tipo de unidades. Estas empresas têm como objetivo definir, entre as unidades CHP e as unidades que apenas geram energia elétrica ou calor, quais devem ser ligadas e os respetivos níveis de produção para satisfazer a procura de energia elétrica e de calor a um custo mínimo. Neste documento são propostos dois modelos de programação inteira mista para o UCP com inclusão de unidades de cogeração: um modelo não-linear que inclui a função real de custo de produção das unidades CHP e um modelo que propõe uma linearização da referida função baseada na combinação convexa de um número pré-definido de pontos extremos. Em ambos os modelos a região de operação admissível não-convexa é modelada através da divisão desta àrea em duas àreas convexas distintas. Testes computacionais efetuados com ambos os modelos para várias instâncias permitiram verificar a eficiência do modelo linear proposto. Este modelo permitiu obter as soluções ótimas do modelo não-linear com tempos computationais significativamente menores. Para além disso, ambos os modelos foram testados com e sem a inclusão de restrições de tomada e deslastre de carga, permitindo concluir que este tipo de restrições aumenta a complexidade do problema sendo que o tempo computacional exigido para a resolução do mesmo cresce significativamente.Conventional thermal power plants convert only part the of the energy resulting from fuel consumption in electric power, while a significant part is wasted as heat loss Combined Heat and Power (CHP) units allow to reuse the energy dissipated as heat and make it available for consumption either by domestic or industrial costumers. Electricity and heat production costs of CHP units are represented by a nonlinear function and a feasible operating region that is either convex or non-convex, depending on the unit’s characteristics. For these reasons, modelling CHP units in the context of the Unit Commitment Problem (UCP) has been of particular interest for generation companies that use such units in their operation. The goal of these companies is to decide, among CHP units and conventional thermal units and heat-only units, which should be turned on to meet the electricity or heat demand at minimum cost. In this work, two mixed integer programming models are proposed for solving the UCP with inclusion of CHP units: a nonlinear model that includes the real cost function of CHP units; and a model proposing a linearization of that function through a convex combination of a predefined number of extreme points. In both models, the non-convex feasible operating region of some CHP units is modelled by splitting it into two separate convex regions. Computational experiments performed with both models for multiple instances allowed to verify the effectiveness of the proposed linear model. This model allowed to obtain the optimal solutions achieved by the nonlinear model with significantly lower computational times. Moreover, both models were tested with and without the inclusion of ramp constraints, allowing to conclude tha such constraints increase the complexity of the problem in such a way that the time required solve the problem grows sharply when including these constraints

    Load dispatch optimization of open cycle industrial gas turbine plant incorporating operational, maintenance and environmental parameters

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    Power generation fuel cost, unit availability and environmental rules and regulations are important parameters in power generation load dispatch optimization. Previous optimization work has not considered the later two in their formulations. The objective of this work is to develop a multi-objective optimization model and optimization algorithm for load dispatching optimization of open cycle gas turbine plant that not only consider operational parameters, but also incorporates maintenance and environmental parameters. Gas turbine performance parameters with reference to ASME PTC 22-1985 were developed and validated against an installed performance monitoring system (PMS9000) and plant performance test report. A gas turbine input-output model and emission were defined mathematically into the optimization multi-objectives function. Maintenance parameters of Equivalent Operating Hours (EOH) constraints and environmental parameters of allowable emission (NOx, CO and SO2) limits constraints were also included. The Extended Priority List and Particle Swarm Optimization (EPL-PSO) method was successfully implemented to solve the model. Four simulation tests were conducted to study and test the develop optimization software. Simulation results successfully demonstrated that multi-objectives total production cost (TPC) objective functions, the proposed EOH constraint, emissions model and constraints algorithm could be incorporated into the EPL-PSO method which provided optimum results, without violating any of the constraints as defined. A cost saving of 0.685% and 0.1157% could be obtained based on simulations conducted on actual plant condition and against benchmark problem respectively. The results of this work can be used for actual plant application and future development work for new gas turbine model or to include additional operational constraint

    Metaheuristics for the unit commitment problem : The Constraint Oriented Neighbourhoods search strategy

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    Tese de mestrado. Faculdade de Engenharia. Universidade do Porto. 199

    Influence Types of Startup on Hydrothermal Scheduling

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    The energy costs of a power plant consist of startup cost and cost of power usage. In contrast to the existing literature, this study introduces at startup cost based on the duration of thermal power plant downtime. The approach of startup cost function in this research is done by using startup type. Startup of a steam power plant depends on its condition. Generally, there are three types of startup the power plant when the turbine temperature is still very high, i.e. hot start, very hot start and very-very hot start. This paper uses multistage optimization to solve the problem of hydrothermal scheduling with including the startup type cost in the objective function. The simulation results showed operating cost savings when the objective function for optimization also consider the cost based on startup type i.e. when compared with the optimization result which the objective function does not take the cost of startup type

    Keterlibatan Emisi Pembangkit dalam Aliran Daya Optimal pada Sistem Tenaga Listrik

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    Pada penggunaan bahan bakar, selain didapatkan energi sebagai hasil proses yang diharapkan, juga menghasilkan emisi gas CO2ekivalen (CO2e) karena proses pembakaran bahan bakar tersebut. Emisi CO2e ini seringkali disebut sebagai emisi gas rumah kaca (GRK). Pada penelitian ini dilakukan studi optimasi aliran daya melibatkan emisi GRK dengan fungsi objektif berupa nilai biaya energi dari penggunaan bahan bakar dan biaya kompensasi emisi GRK menjadi satu persamaan deferensial kuadratik untuk setiap pembangkit listrik. Fungsi objektif dari optimasi aliran daya tersebut di atas menggunakan pendekatan stoikiometri pada reaksi pembakaran. Sehingga didapat hubungan antara bahan bakar yang dikonversi menjadi energi listrik dan emisi GRK. Untuk mendapatkan biaya energi sebagai fungsi objektif dapat dicari dengan mengalikan banyaknya pemakaian bahan bakar terhadap harga bahan bakar, dan biaya emisi GRK didapat dengan mengalikan banyaknya emisi dengan harga kompensasi emisi GRK. Pada perhitungan ini, harga bahan bakar pada satu rentang waktu pendek harian diasumsikan tetap, demikian pula dengan biaya kompensasi emisi GRK diasumsikan tetap. Kemampuan konversi energi pada pembangkit listrik berubah terhadap daya yang dihasilkan. Kemampuan konversi energi ini dilihat sebagai fungsi performansi pembangkit listrik dan biasanya juga disebut sebagai fungsi heat rate pembangkit. Nilai heat rate terbaik adalah pada nilai heat rate terendah. Pada nilai daya kecil heat rate bernilai relatif besar jika dibandingkan dengan pada daya nominal, kemudian menurun sampai daya nominal dan naik lagi setelah daya nominal. Nilai heat rate terbaik ada pada nilai daya nominalnya. Pendekatan fungsi heat rate linier pada rentang daya tertentu dapat digunakan untuk membuat model fungsi kuadratik dari model biaya energi yang ramah lingkungan. Pendekatan ini digunakan untuk membuat fungsi objektif biaya energi dan biaya emisi menjadi persamaan deferensial kuadratik dengan variabel berupa daya terkirim. Dengan menggunakan pendekatan stoikiometri dan dengan pendekatan heat rate linier serta harga bahan bakar dan harga kompensasi emisi tetap, maka parameter fungsi biaya bahan bakar dan fungsi biaya kompensasi emisi dapat disatukan menjadi fungsi kuadratik biaya energi yang melibatkan emisi GRK, atau disingkat sebagai fungsi biaya energi. Model fungsi kuadratik dapat didekati dengan range daya keluaran yang berbeda, yakni menggunakan batasan daya nominal dan atau batasan kapabilitas daya pembangkit. Kedua pendekatan menghasilkan parameter kuadratik γ yang berbeda. Pendekatan pada rentang daya minimum sampai pada daya nominal menghasilkan fungsi deterministik kuadratik dengan γ negatif. Pendekatan pada rentang daya dari daya minimum sampai kapabilitas daya menghasilkan fungsi deterministik kuadratik dengan parameter γ positif. Simulasi dengan fungsi objektif biaya energi pada sistem hydrothermal dengan daya pembangkit listrik tenaga hydro (PLTA) 10 % dari total daya sistem kelistrikan, dan menggunakan harga kompensasi emisi sebesar 10 % dari harga bahan bakar serta menggunakan model kuadratik dan batasan daya masing masing pembangkit pada daya nominal, menggunakan optimal power flow didapatkan hasil simulasi biaya energi karena biaya kompensasi emisi GRK tersebut yakni biaya energi menjadi naik sekitar 23 %. Selain fungsi biaya energi dengan variabel berupa daya listrik, pembangkit listrik ketika start-up juga telah menggunakan bahan bakar dan mengeluarkan emisi GRK, sehingga model biaya pemakaian energi primer dan biaya kompensasi emisi dibagi menjadi 2 bagian besar. Yakni, pertama adalah model biaya pemakaian energi primer dan biaya emisi sebelum pembangkit listrik mengirimkan daya ke jaringan yang dikenal sebagai biaya start-up. Kedua adalah model biaya ketika pembangkit listrik telah mengirim daya ke jaringan yang pada penelitian ini disebut sebagai model biaya energi. Biaya start-up dapat dikelompokkan berdasarkan waktu keluar dari sistem yakni pendekatan status pembangkit yang berkaitan dengan jenis start-up. Sehingga variabel biaya energi mengikutkan biaya jenis start-up menjadi terdiri dari dua variabel yakni variabel diskrit (biner) dan variabel kantinyu. Biaya start-up dapat dikelompokkan berdasarkan lamanya waktu ketika pembangkit telah keluar dari sistem kelistrikan, yakni cold start, warm start dan hot start. Pada periode tinjauan harian seperti pada penjadwalan pembangkit listrik, model biaya start-up dikelompokkan menurut jenis start-up, yakni hot start, very hot start. Pada penelitian ini jenis start-up pada penjadwalan harian dikelompokkan menjadi 3 jenis yakni hot start, very hot start. Model berdasarkan jenis biaya start-up pada penelitian ini mempunyai variabel fungsi biner berupa status pembangkit listrik. Nilai biaya pemakaian energi start-up berdasarkan lamanya status keluar dari sistem ditentukan dengan pengelompokan jenis start-up tersebut. Very very hot start berbiaya paling murah karena pembangkit listrik masih sangat panas, very hot start lebih mahal beberapa kali karena pembangkit mulai menurun suhunya dan hot start jauh lebih mahal karena harus memanaskan mesin cukup lama. Penerapan model biaya energi pada penelitian ini dilakukan pada penjadwalan harian sistem kelistrikan hydrothermal. Pada sistem hydrothermal diketahui bahwa sistem kelistrikan disokong oleh dua jenis pembangkit listrik, yakni PLTA dan pembangkit listrik tenaga thermal (PLT-Thermal). PLTA diketahui energi primernya bergantung pada kecukupan persediaan air pada periode tertentu. Pada musim kemarau kecukupan persediaan air terbatas, sehingga perlu dilakukan maksimasi persediaan air. Sedangkan pada PLT_Thermal dengan persediaan energi primer bahan bakar dianggap tak terbatas, tetapi harga bahan bakar dan harga kompensasi emisi GRK mempunyai biaya yang relatif mahal jika dibandingkan terhadap biaya energi primer PLTA, maka perlu dilakukan minimasi biaya energi sehingga penjadwalan harian menjadi murah dan selalu dapat memenuhi permintaan beban dengan baik. Dua jenis fungsi objektif tersebut pada penelitian ini dilakukan pemisahan dalam optimasinya berdasarkan batasan yang berbeda. Algoritma pemisahan pada penelitian ini disebut algoritma multistage. Langkah-langkah penjadwalan pada algoritma multistage disimulasikan dalam durasi pendek harian, dengan urutan optimasi pertama adalah langkah maksimasi penggunaan air pada PLTA. Langkah pertama ini mendapatkan daya aktif setiap unit PLTA pada setiap jam selama 24 jam. Langkah kedua adalah penentuan status PLT-Thermal dengan minimasi biaya energi dan jenis start-up dari setiap pembangkit. Hasil langkah kedua ini adalah status dari setiap PLT Thermal pada setiap jam selama 24 jam dari penjadwalan harian. Langkah ketiga adalah menentukan besar daya yang dibangkitkan dari setiap pembangkit listrik yang telah terpilih (berstatus 1) pada langkah sebelumnya dan batasan maksimal daya pada setiap unit PLTA pada setiap jam-nya serta dengan memperhatikan batasan sistem kelistrikan. Menentukan besarnya daya pada setiap jam dilakukan dengan menggunakan optimal power flow (OPF) yakni dengan fungsi objektif minimasi biaya energi, karena bentuk fungsi sudah determinsistik maka metoda numerik dengan interior point dapat digunakan pada langkah ini. Minimasi biaya energi dengan OPF dilakukan pada setiap jam dari penjadwalan harian. Langkah keempat adalah menghitung biaya energi dengan memperhatikan biaya start-up dan biaya pengiriman daya ke sistem kelistrikan dengan memasukkan daya yang dihasilkan dari OPF. Hasil perhitungan penjadwalan harian sistem hydrothermal dengan memperhatikan jenis start-up setiap pembangkit, yakni jika dibandingkan dengan menggunakan metoda optimasi yang lain, dalam hal ini optimasi dengan de-commitment unit interor point solver (DU-IPS) dioptimasi pada rentang waktu selama 24 jam, ternyata algoritma multistage menghasilkan biaya energi harian lebih hemat sekitar 2 % dan daya rerata selama satu hari lebih rendah sekitar 1 %. ========================================================================================================= In the use of fuel other than energy obtained as the result of the expected process, also produces CO2-equivalent gas emissions (CO2e) that is due to the combustion process of these fuels. These CO2e emissions are often referred to as greenhouse gas (GHG) emissions. In this research, a power-optimization study involving GHG emission with objective function is the primary energy cost of fuel combined with the cost of GHG emission compensation into a quadratic differential equation. The objective function of the power flow optimization above-mentioned uses a stoichiometric approach to the combustion reaction. So we get the relationship between fuel that is converted into electrical energy and GHG emission. To obtain energy costs as an objective function can be sought by multiplying the amount of fuel used for fuel prices, and GHG emission costs obtained by multiplying the number of emissions at the price of GHG emission compensation. In this calculation, the fuel price at a short time span is assumed to be fixed, as is the cost of GHG emission compensation assumed to be fixed too. The power conversion for the power plant changes with the power generated. This energy conversion is seen as a performance function of a power plant and is also commonly referred to as a power plant heat rate function. The best heat rate is at the lowest heat rate. At the value of small power production then the heat rate is relatively large when compared to the nominal power. Then the value of the heat rate decreases to nominal power generation and rises again after nominal power. Thus, the best heat rate value is at its nominal power value. The linear heat rate approximation approach to a given power production range can be used to create a quadratic function model of the environmentally friendly energy cost xii model. This approach is used to make objective function of energy cost and emission cost into a quadratic differential equation with variable is electric power. Using the stoichiometric approach and with the linear heat rate approach as well as fuel prices and fixed emission compensation prices, the fuel cost function parameters and the emission compensation cost function can be composed into a quadratic energy cost function. Quadratic function models can be approximated by different output power ranges, using nominal power constraints and/or power plant capability constraints. Both approaches produce different quadratic parameters. The approach to the minimum power range up to nominal power produces a quadratic deterministic function with γ negative. The approach of the power range from minimum power to its power capability produces a quadratic deterministic function with γ positive. Simulation with the objective function of energy cost in a hydrothermal system with hydropower is 10% of the total power, and then use the price value of emission compensation 10% of the fuel price and use the quadratic model with the power limits of each plant at nominal of power, simulation results obtained of energy cost because the cost of GHG emission compensation that cost is increased by 23%. In addition to the energy cost function with variables is electric power, power plants at start-up also have used fuel and emitted GHG emissions, so the primary energy cost model and the cost of emission compensation are divided into 2 major sections. Namely, first is the model of the cost of primary energy use and the cost of emissions, namely before the power plant sends power to a grid known as start-up costs. The second is the cost model when the power plant has sent power to the grid which in this study referred to as the energy cost model. Start-up costs can be grouped based on time of the shutting down from the grid ie the power plant status approach related to the start-up type. So the energy cost variable includes the startup type cost consisting of two variables ie discrete (binary) and continuous variables. Start-up costs when based on the length of time when the power plant has exited from the electrical system, it can be grouped into cold start, warm start and hot start. In daily review periods such as power plant scheduling, the start-up cost model is grouped by start-up type, ie hot start, very hot start. In this study type of xiii start-up on the daily scheduling grouped into 3 types of hot start-up, that are hot start, very hot start and very very hot start. The model based on the type of start-up cost in this study has a variable binary function in the form of power plant status. The cost value of start-up energy consumption based on the length of the exit status from the system is determined by the grouping of the start-up type. Very very hot start the cheapest cost because the power plant is still very hot, very hot start more expensive several times because the plant starts to decrease its temperature and hot start is much more expensive because it must heat the machine long enough. The application of energy cost model in this research is done on daily scheduling of hydrothermal electrical system. In the hydrothermal system, it is known that the electrical system is supported by two types of power plants, namely hydropower and thermal power plant. The hydropower plant is known that its primary energy depends on the adequacy of water supply for a certain period. In the dry season the adequacy of water supply is limited, so there is a need to maximize water supply. While the thermal power plant with primary fuel energy inventory is considered unlimited, but the price of fuel and GHG emission compensation prices have a relatively expensive cost when compared to the primary energy cost of hydropower, it is necessary to minimize energy costs so that daily scheduling so that it becomes cheap and can always meet the load demand. The two types of objective functions in this study are separated in their optimization based on different constraints. The separation algorithm in this research is called multistage algorithm. The scheduling steps on the multistage algorithm are simulated in daily short duration, with the first optimization sequence being the maximization step of water use on hydropower. This first step gets active power of each hydropower unit every hour for 24 hours. The second step is the determination of thermal power plant status with the minimization of energy cost and the start-up type of each plant. The result of this second step is the status of each thermal power plant at every hour for 24 hours from daily scheduling. The third step is to determine the amount of power generated from each of the selected power plants (the status is 1) in the previous step and the maximum power limit on each hydropower unit at each hour and with due regard to the electrical system constraints. Determine the amount of xiv power at each hour is done by using optimal power flow (OPF) ie with objective function minimization of energy costs because the form of the function has been deterministic then the numerical method with the interior point can be used in this step. Minimization of energy costs with OPF is done at every hour of daily scheduling. The fourth step is to calculate energy costs by taking into account the start-up costs and the cost of delivering power to the electrical system by entering the power generated from the OPF. The results of daily hydrothermal system scheduling calculation by considering the start-up type of each plant, ie, when compared with other optimization methods, in this case optimization with the de-commitment unit of interor point solver (DU-IPS) for 24 hours, it turns out the multistage algorithm produces daily energy costs more sparingly about 2% and average power for one day is lower by about 1%

    Mekaanisen massan tuotannon ja energiahallinnan optimointi paperitehtaalle, jolla on integroitu CHP-voimalaitos

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    The pulp and paper industry is facing global competition, where companies are working to lower their production costs. Energy consumption plays an important role in these costs. There is much interest into lowering the electricity costs of mills through demand side management. This Thesis is a case study of a mechanical pulp and paper mill with integrated CHP production. The case site is modeled with focus on the critical dependencies between pulp, paper, and CHP production. The purpose of the model is to analyze the mill’s capacity of demand side management, and the total costs of executing regulating power bids in the mill site. The production scheduling of mechanical mass is studied through a mixed integer linear model. The model is based on the processes of the mill site, considering the balances of steam, electricity, heat, and mechanical mass. Paper production scheduling is not in the scope of the model. The model is utilized to calculate the increase of production costs in case a regulating power trade is made. The model creates an optimal mechanical mass production schedule for a 24 hour period. It is then used to modify that schedule based on a hypothetical regulating power bid that is accepted on the first hour of the modeling period. The cost difference between the resulting two schedules is calculated, denoting the real cost of regulating in that scenario. This analysis is repeated for a number of real periods in terms of electricity price and district heating demand. The model generates realistic production schedules of mechanical mass. Upregulating power trades are found to cause moderate costs, but there is significant variation. It is noted that the co-planning of the mill and power plant plays an important role in the results. Its design allows the model to be used for various purposes in addition to what is presented in this Thesis.Sellu- ja paperiteollisuus on globaalissa kilpailutilanteessa, jossa yritykset pyrkivät alentamaan tuotantokustannuksiaan. Energian kulutuksella on suuri rooli näissä kustannuksissa. Sähköenergiakustannusten alentaminen kulutusjouston avulla herättää paljon kiinnostusta. Tässä diplomityössä esitellään case-tutkimus mekaanista massaa ja paperia valmistavasta tehtaasta, jolla on integroitu CHP-voimalaitos. Kohteena oleva tehdas voimalaitoksineen mallinnetaan, keskittyen tärkeimpiin riippuvuuksiin massan, paperin ja energian tuotantoprosessien välillä. Tavoitteena on analysoida tehdaskokonaisuuden kapasiteettia kulutusjouston tekemiseen sekä säätösähkötarjousten toteuttamisen kustannuksia. Mekaanisen massan valmistuksen aikataulutusta tutkitaan lineaarisen sekalukuoptimointimallin avulla. Malli perustuu tehdaskokonaisuuden prosesseihin, joista huomioidaan taseet höyrylle, sähkölle, lämmölle ja mekaaniselle massalle. Paperintuotannon aikataulutus ei kuulu mallin piiriin. Työssä esitetään, miten mallia voi käyttää säätökaupan aiheuttamien lisäkustannusten laskemiseksi. Mallin avulla luodaan optimaalinen mekaanisen massan tunneittainen tuotantoaikataulu vuorokauden jaksolle. Tätä aikataulua muokataan edelleen mallin avulla kuvitteellisen säätösähkötarjouksen perusteella, joka hyväksytään mallinnusjakson ensimmäisellä tunnilla. Vertaamalla tuloksina saatujen kahden aikataulun kustannuksia voidaan arvioida säädön todellinen kustannus. Tämä analyysi toistetaan useissa eri tilanteissa todellisilla sähkön hinnoilla ja kaukolämmön tarpeilla. Malli tuottaa realistisia aikatauluja mekaanisen massan tuotannolle. Ylössäätökauppojen todetaan aiheuttavan kohtuullisia kustannuksia, mutta vaihtelu on suurta. Huomataan, että paperitehtaan ja voimalaitoksen yhteissuunnittelu on tärkeässä roolissa tuloksissa. Mallin rakenne mahdollistaa sen käyttämisen tässä työssä esitettyjen lisäksi myös muihin tarkoituksiin

    OPERATIONAL RELIABILITY AND RISK EVALUATION FRAMEWORKS FOR SUSTAINABLE ELECTRIC POWER SYSTEMS

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    Driven by a confluence of multiple environmental, social, technical, and economic factors, traditional electric power systems are undergoing a momentous transition toward sustainable electric power systems. One of the important facets of this transformation is the inclusion of high penetration of variable renewable energy sources, the chief among them being wind power. The new source of uncertainty that stems from imperfect wind power forecasts, coupled with the traditional uncertainties in electric power systems, such as unplanned component outages, introduces new challenges for power system operators. In particular, the short-term or operational reliability of sustainable electric power systems could be at increased risk as limited remedial resources are available to the operators to handle uncertainties and outages during system operation. Furthermore, as sustainable electric power systems and natural gas networks become increasingly coupled, the impacts of outages in one network can quickly propagate into the other, thereby reducing the operational reliability of integrated electric power-gas networks (IEPGNs). In light of the above discussion, a successful transition to sustainable electric power systems necessitates a new set of tools to assist the power system operators to make risk-informed decisions amid multiple sources of uncertainties. Such tools should be able to realistically evaluate the hour- and day-ahead operational reliability and risk indices of sustainable electric power systems in a computationally efficient manner while giving full attention to the uncertainties of wind power and IEGPNs. To this end, the research is conducted on five related topics. First, a simulation-based framework is proposed to evaluate the operational reliability indices of generating systems using the fixed-effort generalized splitting approach. Simulations show improvement in computational performance when compared to the traditional Monte-Carlo simulation (MCS). Second, a hybrid analytical-simulation framework is proposed for the short-term risk assessment of wind-integrated power systems. The area risk method – an analytical technique, is combined with the importance sampling (IS)-based MCS to integrate the proposed reliability models of wind speed and calculate the risk indices with a low computational burden. Case studies validate the efficacy of the proposed framework. Third, the importance sampling-based MCS framework is extended to include the proposed data-driven probabilistic models of wind power to avoid the drawbacks of wind speed models. Fourth, a comprehensive framework for the operational reliability evaluation of IEPGNs is developed. This framework includes new reliability models for natural gas pipelines and natural gas-fired generators with dual fuel capabilities. Simulations show the importance of considering the coupling between the two networks while evaluating operational reliability indices. Finally, a new chance-constrained optimization model to consider the operational reliability constraints while determining the optimal operational schedule for microgrids is proposed. Case studies show the tradeoff between the reliability and the operating costs when scheduling the microgrids

    Large-scale unit commitment under uncertainty: an updated literature survey

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    The Unit Commitment problem in energy management aims at finding the optimal production schedule of a set of generation units, while meeting various system-wide constraints. It has always been a large-scale, non-convex, difficult problem, especially in view of the fact that, due to operational requirements, it has to be solved in an unreasonably small time for its size. Recently, growing renewable energy shares have strongly increased the level of uncertainty in the system, making the (ideal) Unit Commitment model a large-scale, non-convex and uncertain (stochastic, robust, chance-constrained) program. We provide a survey of the literature on methods for the Uncertain Unit Commitment problem, in all its variants. We start with a review of the main contributions on solution methods for the deterministic versions of the problem, focussing on those based on mathematical programming techniques that are more relevant for the uncertain versions of the problem. We then present and categorize the approaches to the latter, while providing entry points to the relevant literature on optimization under uncertainty. This is an updated version of the paper "Large-scale Unit Commitment under uncertainty: a literature survey" that appeared in 4OR 13(2), 115--171 (2015); this version has over 170 more citations, most of which appeared in the last three years, proving how fast the literature on uncertain Unit Commitment evolves, and therefore the interest in this subject
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